Social Sciences as Hokum
HOW I USE IT
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The SPSS cure! Forecasting Sales
Growth in the NHS and social
research in the public sector.
By Tassadaque Masood
Objectives
• My experiences of using SPSS
• Forecasting-NHS Supply Chain
• Nottingham University Hospitals NHS Trust
A&E Forecasting Project.
• BSA 2007 Data Set
• Conclusions & Recommendations
• Questions
Forecasting
Data Challenges
• Time series data- each case (row) represents a set
of observations at a different time, and the length
of time between cases is uniform.
• Create 3 new time series variables as functions of
existing time series variables.
• Generate date variables to establish periodicity
and to distinguish between historical and
forecasting periods.
NHS Supply Chain-Advantages
• Uncover data patterns
• Predict trends and forecast future events
• Effective planning tool against a competitive
backdrop which is constantly changing- e.g. flu
pandemic.
• Effective staff planning at peak periods.
• Access, manipulate and model data types, from
all sources and show different methods
according to business need.
Nottingham University Hospitals NHS Trust
Data Challenges
• Time series data- each case (row) represents a set
of observations at a different time, and the length
of time between cases is uniform-Not the case.
• Create new time series variables as functions of
existing time series variables.
• Generate date variables to establish periodicity
and to distinguish between historical, validation,
and forecasting periods.
• Missing Values
Process Overview
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Data Integration with SPSS
Data Cleansing using SPSS
Data Summary
SPSS Syntax- Create Periodicity
Forecasting Model
Data Validity Checks
Data Interpretation
Generating descriptive statistics
independently of frequencies
• Select Analyze, and then
Descriptive Statistics...
from the main menu.
• When the sub- menu
appears select Descriptive…
to call up the Descriptive
dialogue box.
• When you are ready to run
your descriptive statistics
from the Descriptive
dialogue box click on OK.
Example
SPSS Syntax- Create Periodicity-Define
Dates
• The Define Dates
dialog box allows you
to generate date
variables that can be
used to establish the
periodicity of a time
series and to label
output from time
series analysis.
SPSS Syntax- Create Periodicity-Define
Define
Dates
Dates
• From the menus choose:
– Data
– Define Dates
• Select a time interval from the Cases Are
list.
• Enter the value(s) that define the starting
date for First Case Is, which determines
the date assigned to the first case.
British Social Attitudes 2007
Compare means
• Another useful function
that SPSS possesses is the
ability to compare the
means for 2 variables.
• Go to the Analyze menu
and select Compare
Means and then Means…
from the submenu.
Compare means
• Select the variable that
deals with attitudes to
censorship and the
variable for sex of
respondent and paste
them into the correct
parts of the window so
that they look like the
example here.
• Click on OK and SPSS will
produce the following
output for you.
Compare means
• Given that the scoring
system in relation to this
(and the other variables
concerning moral issues)
variable, it runs from 1
for those who say that
they ‘strongly agree’
through to 5 for those
who say that they
‘strongly disagree’,
• The lower the mean
score, the more
agreement that there is
with the statement.
Manipulating and transforming
data: the Recode function
• Modify an existing
variable by looking at
how we can create a
new variable that will
put the respondents’
ages in age bands.
• Go to the Transform
menu and select
Recode into Different
Variables
• This will call up the
Recode into Different
Variables dialogue box.
• Select the Age variable
from the source list and
place it in window in the
middle of the dialogue
box using the arrow
button or by dragging
and dropping it.
• Click on the Old and New
Values button to call up
the Old and New Values
dialogue box.
• Click where it says
‘Range, LOWEST
through value’ to put
a dot in the white
circle.
• Then on the ‘new
value’ side of the
dialogue box type 1
into the space where
it says ‘value’.
• Click on the Add button
and ‘lowest thru 24 → 1’
appears in the ‘old →
new’ area.
• This operation has
recoded all of the ages
from the youngest
respondent through to
those aged 24 and put
them all in an age group.
• Using the ‘Range’ part of
the ‘old value’ side of the
dialogue box, recode the
age bands in groups of 10
years, i.e., 25 - 34 up to 75
- 84.
• As you progress through
these age groups you will
need to give them new
values from 2 onwards up
to 7.
• Finally, use the ‘Range,
value through HIGHEST’
function to recode all of
those over the age of 85
into an eighth age category.
• Click on Continue to
return to the Recode
into Different Variables
dialogue box.
• Now, give the new
variable that you are
creating a new name
(age2, for example)
and a new variable
label (age groups), and
then click on the
Change button.
• Click on the OK button
to run the recode.
• Click in the Numeric cell
and change the width and
decimals to 1 and 0
respectively.
• Click in the values cell and
assign the values and value
labels that correspond with
the recoded categories.
• When you have done this
your labels dialogue box
should like the example
shown here.
• Finally, assign your new
variable an ordinal level of
measurement.
• Run a frequency to see the
new age group variable.
• This new recoded age variable
will be very useful when it
comes to conducting bivariate
analysis using cross-tabulation
tables.
Scale
• Recode function like this to
make some data more
manageable, bear in mind
that whilst you will make it
easier for the data to be used
you are likely to downgrade
their level of measurement
and thus make more
sophisticated analysis less
likely.
Ordinal
• In this example, a scale
variable has been reduced to
an ordinal variable.
• To do this you would recode
1 and 2 (‘agree strongly’
and ‘agree’) into a single
agree category = 1.
• Note that while you would
need to code 2 = 1, to
leave 1 = 1 you can use the
‘all other values’ and ‘copy
old values’ buttons in the
recode dialogue box.
•The
previous
middle
neutral
category of ‘neither agree nor
disagree’ would be recoded from 3 to
a new middle value of 2. Finally, 4
and 5(‘disagree’ and ‘disagree
strongly’) would be recoded into a
single disagree category or 3.
Recoding to allow for higher
statistical analysis
• Let us look at the example of the variable that deals with the
highest education qualification that is held by respondents.
• However, there are two values that serve to interrupt this
flow: ‘foreign or other’ and ‘don’t know/refusal/not
applicable’.
• To recode this variable
accordingly we would
need to:
• Recode the values 6
(foreign or other) and 8
(don’t know/refusal/not
applicable) as missing.
• Recode 7 (no
qualification) as 6, thus
maintaining a flowing
numerical order from 1
(degree) to 6 (no
qualification).
• Copy all the other values
across as they were in
the original variable.
• Having removed 9.6% of respondents who
were coded 6 or 8 as missing, our new
frequency table shows an ordinal variable.
Computing a new variable
• The Compute command can be used to transform
the values of one or more existing variable(s) into
a new variable.
• Let’s work with the set of six variables at the end
of the data set that looks at people’s attitudes
towards a number of statement concerning moral
values.
• By using the Compute function, however, it is
possible to get an overall view of attitudes
towards moral issues.
• Go to the Transform menu
and select Compute Variable
and the Compute Variable
dialogue box will appear.
• Click in the Target Variable
box at the top left of the
dialogue box and type in the
name of the new variable
that you will compute. Let’s
call this variable morals.
• Give your new variable a
variable label by clicking on
the Type & Label button
below the Target Variable
box, typing in an appropriate
variable label, and then
• Click on Continue.
• Go to the source list of
variables which contain
the six variables that
deal with moral issues.
• Select the first of these
for GB values] and
paste it into the
Numeric Expression
box.
• Click on the + button
and then repeat the
process with the
remaining variables..
• Then click on OK.
BSA Example
• From this initial analysis of the new
variable, we can see that the respondents
do tend more towards the authoritarian
than the libertarian end of the scale.
• There are a variety of ways that we could
report this data.
• One option would be to simply add
together a certain number of responses at
either end of the variable.
• For example, while 22.1% of respondents
are in the lowest five categories (6-10), only
0.4% are in the highest five categories (25• 30).
• If we run a means
comparison against age
(grouped), we can also
see that there appears
to be a clear pattern
between moral values
and age.
• The older the age
group the more the
tendency to hold an
authoritarian outlook.
Creating graphics in SPSS
• SPSS gives you the
opportunity to generate
a graphic whenever you
ask it to run a
frequency.
• Go to the Frequencies
command.
• Select the variable the
‘censor’ variable that
deals with respondents’
attitudes towards
censorship and paste it
into the target list.
• Click on the Charts…
button in the dialogue
box.
• Select Pie charts as a
Chart Type, and then
Percentages for the
Chart Values.
• Click on Continue, and
then OK in the
Frequency dialogue box
to run the output.
BSA Dataset-Key Conclusions
• Check level of measurement-Nominal (Sex of
Respondent), Ordinal (logical order e.g. Agree
to Disagree) or Scale.
• Carry out analysis according to level of
measurement.
• The ability to choose the appropriate statistical
test and to see whether the calculations are
• Scale variables=Larger Statistical analysis
• Conscious of a progression from one level of
measurement to another.
Conclusions &
Recommendations
Questions