Spss basics

Chapter One
Analysing Simple Questionnaires: SPSS Basics
Introduction
Aims of the first three chapters
This opening set of three chapters has a number of purposes. Most directly, they teach
you how to analyse simple, data-gathering questionnaires. But this basic goal runs in
parallel with some other goals.
• the chapters introduce you to issues connected with how you structure a dataset
for computer analysis – how you organise it, and how you prepare the data for
input. (This is developed in Chapter 2.)
• the chapters cover a basic set of statistical techniques for describing data
concisely, and in ways that make patterns within the data more salient. These
techniques include the use of descriptive statistics such as means, standard
deviations, cross-tabulations etc.
• the chapters also cover some of the dataset basics, such as how to label what you
do effectively, and how to use the power of SPSS to create new variables, or to
recode the values you initially input
• the chapters show you how to display data visually, using a number of different
formats, such as bar charts, and pie diagrams
• the chapters show you how to save the computer output that you generate, and use
this output in other documents
The Plan of the Three Chapters
The next three chapters, starting with this one, are organised to progressively extend
the techniques you can use to analyse questionnaires.
In this chapter, Chapter One:
• You are introduced to a “taster” questionnaire, on lateralisation, i.e. a
preference for right or left, and shown how to do some simple analyses. For
this section you will also have to gather some data yourself.
• The main questionnaire dataset for this chapter is described to you. The
questionnaire which is used concerns a survey that was done of the British
ELT profession – to ask people whether they thought it would be worth
establishing a British Institute of English Language Teaching (BIELT) as a
professional organisation for the field. The questionnaire contains over 50
items, and there are responses from nearly 1200 people.
In Chapter Two:
• You are taken through a reduced version of the dataset, with a subset of the total
number of variables, and only 100 cases (i.e. responses from 100 people). This
reduced dataset, though, is enough to teach you how to use the various statistical
and graphing procedures. There are tasks for you to do at all stages.

In Chapter Three:
• You are given the entire dataset to analyse, with a series of progressively more
ambitious tasks. Each task is followed by feedback.
• You revisit the lateralisation questionnaire, with data you should have gathered by
then, and compare your results to those obtained in a sample already collected.
Presentation Conventions
This chapter (and others in the course) follow a set of consistent presentation
conventions. The chapters contain four different sorts of information, and each of
these is signalled in some way. The conventions are:
• plain text: where there is simply text (not boxed, or caused to stand out in any
way) you are dealing with simple exposition. These sections are the central
material in the course. They are to be read, thought about, and absorbed.
• tasks: where you see boxed, shaded normal text, you are being set a task to do.
The task is located at an appropriate point in the chapter and you should attempt
to complete the task there and then, by accessing the computer, using SPSS, and
completing the tasks and answering the questions
• feedback: most tasks will be followed by feedback. This feedback is
enclosed in a box bordered by a single line and the text is in Univers font
(as is the rest of this bullet point). Feedback sections usually contain the
output the task was intended to produce, together with a commentary on
the output. Note that the tasks in the first chapter are unusual in that they
are not accompanied by feedback. This is because they are more
straightforward in nature, and are not output-focussed.
• reflection: from time to time you will encounter blocks of text, headed Reflection,
always italicised, and enclosed within a double-line border. These sections are
generally located after you have completed a task, and probably received
feedback. They ask you to stand back and reflect upon what you have done.
Actual tasks require you to do specific things, (and, with luck, find out reasonably
interesting things). But the techniques which the tasks push you to use are general,
and can be applied, by you, to other datasets, and to answer other questions. The
reflection sections push you to appreciate the generality of the techniques you
have learned, so that you can transfer them to other situations.

Building and Inputting a Questionnaire Dataset
The first dataset will only be used briefly. It is the least serious in the entire course,
and is only intended to introduce you to working with data. Consider, first, the
following set of questions:
Which side do you prefer? Tick the side that applies to
you.
Left No
Pref.
Right
1. With which hand do you draw?
2. Which hand would you use to throw a ball to hit a
target?
3. In which hand would you use an eraser on paper?
4. With which foot would you kick a ball to hit a target?
5. Which hand removes the top card when you are dealing
from a pack?
6. If you wanted to pick up a small stone with your toes,
which foot would you use?
7. If you had to step up onto a chair, which foot would you
place on the chair first?
8. Which eye would you use to look through a telescope?
9. Which is your dominant eye? (Hint: point at something,
then close each eye in turn. You should find that your
pointing finger only lines up with one eye.)
10. If you wanted to listen to a conversation behind a closed
door, which ear would you place against the door?
11. Into which ear would you place the earphone of a small
radio?
___
___
___
___
___
___
___
___
___
___
___
___
___
___
___
___
___
___
___
___
___
___
___
___
___
___
___
___
___
___
___
___
___
These questions are all concerned with the issue of which “side” of your body you
prefer (or, to use the more specialist term, “lateralisation”). Notice here that there are
four questions on “handedness”, three questions on “footedness”, and two each on
“eyedness” and “earedness”. In other words, the questionnaire implies that it is not
entirely safe to have only one item in each of these areas, and that lateralisation may
not be simple, i.e. the fact that you have a right-hand preference does not guarantee
that you have a right-foot preference, and so on. Lateralisation, in other words, is
thought to be a complex “more or less” issue rather than simply “all or none”.
Imagine handling data generated by this questionnaire. The first decision that you
have to make is how to give numeric values to the answers. In that respect, let’s
imagine that you choose to represent left (hand, foot, eye, ear) by ‘1’, no preference
by ‘2’, and right by ‘3’. In effect, what you are doing here is coding the questionnaire
responses, so that an easy-to-handle numerical code replaces the original answer.
This numerical code can then be accessed more easily by a statistical program.
This leads to the next question – how to represent or arrange this data?
In effect, the standard manner of doing so is to imagine a matrix, in which:

• each row represents a person
• each column represents one of the questionnaire items (or variables, as we
shall term them).
You could arrange this data, on paper, as follows (where, for ease of exposition, we
are working with the answers from the first six people only):
ID Q1 Q2 Q3 Q4 Q5 Q6 Q7 Q8 Q9 Q10 Q11
a 3 3 3 3 3 3 3 1 3 3 3
b 3 3 3 3 3 3 3 3 3 3 3
c 3 3 3 3 3 3 1 9 3 2 1
d 1 1 1 1 1 1 1 1 1 1 3
e 3 3 3 3 3 3 3 3 3 3 3
f 3 3 3 1 1 3 1 1 3 3 1
Notice that the first row and the first column in this matrix have been taken up with
non-data elements. The first row shows an identifier (rather minimally in this case)
for each of the variables or measures, while the first column shows an identifier (also
minimal) for each person who has filled in the questionnaire. The data then appears
in the remaining space in this matrix (which basically resembles a spreadsheet).
Remember that, regarding the identifiers in the first column:
a) it’s good practice to put an identifier in the first column. When you are inputting
data, you often cannot imagine forgetting what it all means. It is amazing though,
how the passage of relatively brief periods of time erases this memory. So, it is
very helpful to put in an identifier which will enable you to come back to this
data, see it in the first column, and remember what on earth it means! Note that,
even beyond this first column identifier, it’s worth creating a completely separate
word-processed file in which you jot notes on the dataset (a separate file for every
dataset you have), precisely in order to help you remember later what decisions
you have made, and why. This “codebook” file could then contain the key
information, showing which identifier code matches up with which real person. It
functions as a diary of all the things you have done.
b) you don’t have to use single letters in this way. The real purpose is to have a
unique identifier. You may prefer not to use just letters (which presuppose that
you have already got some sort of codebook telling you what or who the letters
refer to), and instead use names or more complicated codes, perhaps because they
convey something more real about the source of the data (and also cover more
than 26 possibilities!).
Finally, we get to look briefly at the data itself. You can see that it captures the way
that most of the people have a “right” laterality preference (since most responses are
coded ‘3’), while there is one left-lateralised person (person d) and one rather
ambidextrous person (person f). The data also suggests that in most cases (but not
person e) there are slight intrusions of the “other” laterality, to introduce a little
variety.

Last of all, notice a couple of ‘different’ numbers. The first of these is in the third
data line, for Question 8. This cell in the data matrix is coded ‘9’. The intention here
is to designate person “c” as having failed to respond. The computer will later be
instructed that ‘9’ represents a missing value, and so will “know” to ignore it in all
calculations. What I am imagining happened here is that this respondent, for whatever
reason, chose not to answer this question (about “the eye you use to look through a
telescope”). So, a coding value was chosen which could not naturally occur, and this
is used, (rather than leaving a blank), to signal that there is a missing value. Another
cell contains the coding ‘2’. This is meant to represent a case where someone has
replied ‘No preference’, and which is taken to mean someone can use either right or
left. In other words, we are not dealing with a completely missing value, (where no
response is given), but with a slightly different response, which doesn’t fit into the
expected categories. This may be interesting, and by using a coding like ‘2’, we can
check whether there are patterns in the answers where people say that they have no
laterality preference.
There is one more thing that we need to consider at the outset. We haven’t
considered why this questionnaire is being used (and in all honesty nor will we,
seriously). But taking a fairly superficial approach, let’s say we want to gather data on
lateralisation because we have some hypotheses about the distribution of
lateralisation, i.e. we think that there may be some aspects of lateralisation which are
different for certain categories of people.
Here are a few suggestions:
• sex
• age
• nationality
• familial lateralisation
Now the point here is not that it is seriously being proposed that these factors are
crucial in an investigation of lateralisation. They are simply convenient for
exposition. So, if you accept this dubious premise, let’s see how we would plug these
factors into our questionnaire.
Sex This is relatively easy. Let’s imagine that we will code one sex ‘1’
and the other ‘2’. (I leave it you to decide which order you prefer.)

Age This is a little more difficult. Of course, you could input data on the
age, in years, of each subject. But for present discussion, let’s
imagine that this would be inefficient, in the sense that we might get
very few people with any particular age. Typically, in this
circumstances, the “raw” data will be recoded, and what will be
entered into the datafile will be a number referring to a range of ages.
For example, we might try:
Under 20 1
20-29 2
30-39 3
40-49 4
50+ 5
This system will allow us to recode actual ages within these five
categories, and each of the categories is therefore more likely to have
a sizable number of “cases”.
Nationality Clearly one cannot have a code for every country. It would be too
detailed, and essentially meaningless. So we would need to rationalise
in some way. So, we might try:
North America 1
Central and South America 2
Europe 3
Africa 4
Middle East 5
Asia 6
Australasia 7
Or alternatively any of these could be broken down more finely, or, if
thought to be more appropriate, some of these categories could be
conflated.
Familial
Handedness
If there is a genetic component in lateralisation, it may be interesting
to see whether, even if someone is right-handed, there are other
members of someone’s immediate family who are left-handed. The
next question is then to ask how far the family “net” goes. For our
purposes, we will simply add the question:
What proportion of your parents, grandparents, brothers or sisters
are left handed?
And note: this question will generate a simple number as an answer,
to two decimal places, e.g. 1.00 if 100% of your relatives are left-
handed, 0.07 if only one in fourteen is left-handed.

Assuming that we have this information, we now need to think about how to
incorporate it within our dataset. A fairly general response would be as follows:
ID Sex Age Nation
-ality
Fam.
H’ness
Q1 Q2 Q3 Q4 Q5 Q6 Q7 Q8 Q9 Q10 Q11
a 1 2 2 0.00 3 3 3 3 3 3 3 1 3 3 3
b 1 2 4 0.08 3 3 3 3 3 3 3 3 3 3 3
c 2 3 3 0.15 3 3 3 3 3 3 1 9 3 2 1
d 2 2 6 0.32 1 1 1 1 1 1 1 1 1 1 3
e 1 1 6 0.10 3 3 3 3 3 3 3 3 3 3 3
f 2 3 4 0.18 3 3 3 1 1 3 1 1 3 3 1
Bear in mind, again, that this dataset, in real life, would be likely to contain far far
more responses than simply six. Once again, six is a manageable number to display,
which is its only good feature!
Here we see that four new columns have been inserted immediately after the ID
column and before the scores for each questionnaire item rating. Two points are worth
making about this:
• placing what might be called “organising variables” in columns at this point in the
line makes good sense in that they are extremely visible
• now that these variables are coded, they can be used as the organising basis for a
number of useful analyses designed to bring out patterns in the data. That they are
placed fairly prominently on the line helps in this process.
As a result of organising the data in this way, (and imagining that you had access to a
dataset of (say) 200 cases), you could now:
• explore whether men and women differ in their laterality preferences
• explore whether there is any sort of difference in laterality preferences as the
figures relate to older people (perhaps right-handers who are older were pressured
into losing their natural left-handedness early in life)
• explore whether laterality preferences are related to (fairly grossly defined)
national origin (e.g. is the proportion of left handers constant across populations?)
• explore (more complicatedly) whether men and women of different ages differ in
laterality preferences (perhaps men or women who are older were pressured
differently to lose their left-handedness)
• explore whether people who have left-handers in their family show different
lateralisation patterns to those who do not.

The full form of the questionnaire is given below. Although presented as a task, the
material isn’t shaded, because it is assumed that when you print out this
questionnaire, you won’t want to give out people a shaded version!
Task 1.1: Gathering Lateralisation Data
Sex: _____________ Age: _______________
Nationality: _____________
Family Handedness: _____________
(Think of the ten closest members of your family, in the order: brothers and
sisters, parents, children, grandparents, cousins. Then count the number of left-
handers in this group, and give this number out of ten. If you cannot think of ten
such (blood) relatives, i.e. no-one related only by marriage, then count as many
as you can, and give the result, e.g. 2/7, indicating two left-handers out of seven
relatives.)
Which side do you prefer? Tick the side that
applies to you.
Left No
Pref.
Right
1. With which hand do you draw?
2. Which hand would you use to throw a ball to hit a
target?
3. In which hand would you use an eraser on paper?
4. With which foot would you kick a ball to hit a
target?
5. Which hand removes the top card when you are
dealing from a pack?
6. If you wanted to pick up a small stone with your
toes, which foot would you use?
7. If you had to step up onto a chair, which foot
would you place on the chair first?
8. Which eye would you use to look through a
telescope?
9. Which is your dominant eye? (Hint: point at
something, then close each eye in turn. You should
find that your pointing finger only lines up with one
eye.)
10. If you wanted to listen to a conversation behind a
closed door, which ear would you place against the
door?
11. Into which ear would you place the earphone of a
small radio?
___
___
___
___
___
___
___
___
___
___
___
___
___
___
___
___
___
___
___
___
___
___
___
___
___
___
___
___
___
___
___
___
___

Print this questionnaire in multiple copies and try to get it completed by as many
people as you can, but certainly thirty responses. It won’t take anyone longer than
five minutes to complete the questionnaire, so it shouldn’t be difficult to get this
number, or even more. Because of the information that the questionnaire collects, it
would be sensible, if possible, to try to get some variety regarding:
• Sex
• Age
• Nationality
In addition, although it might be easier, avoid getting lots of responses from the same
family, since this will bias the familial handedness results by giving you repeated
versions of the same thing.
We will return to the questionnaire at the end of Chapter Three, so by the time you
reach that point, it would be good if you had completed collecting your data. In other
words, as a suggestion, you should try and get this data collected in the next two to
three weeks.
As you work through Chapters One to Three and acquire relevant SPSS skills, you
should also think about developing a data file so that you can input this data to SPSS.
Guidance is provided in the following pages which will enable you to set up this data
file.
Reflection
In one sense, all we have done so far is very simple. Yet it is a very powerful
simplicity which contains a number of general lessons:
• structuring a dataset into rows (cases, or subjects, or people) and measures (or
variables) is central to any later statistical work. It prepares the ground
immeasurably.
• labelling the rows and columns is very helpful (especially after the passage of any
length of time). We will return to this, and extend it, in Task 1.5.
• having separate measures (in this case of the different aspects of lateralisation) is
what will allow the “fineness” of the statistical explorations which will come
later.
• thinking of relevant “organising” measures (or variables) will be very helpful
later to structure an investigation. At the outset, always try to think of as many of
these as you can. Remember: you can’t analyse data you don’t collect, but you
don’t have to analyse all the data you do collect. In other words, think carefully
about organising variables at the stage where you are planning data collection –
if you don’t, you’ll kick yourself later when you do think of something it would
have been really useful to have asked.
• coding non-numeric data needs a little planning (but probably not that much, in
most cases). Statistical programs have difficulty analysing data:
- which is expressed as text
- which contains a lot of separate values, e.g. age in the present case
For the first of these, e.g. the answers “left” and “right” you need to devise a
word-to-number transfer system (and here we’ve used ‘1’ (left), ‘2’ (no preference),

and ‘3’ (right)). (Sometimes, of course, you’ll have more labels than just two.) For
the second, (e.g. age, nationality), the way to deal with the flood of data is to impose
order on it with a reduced set of categories. In such cases, use any “natural” coding
system that seems relevant (e.g. continents for nationality), and/or try to work in the
range of fewer than ten categories, because more than ten will make the data
unwieldy. Remember: the consequence of your decision now will be played out in the
analyses that are possible later.
Task 1.2
The datasets in this course are almost all given to you, on computer disc or CD-Rom
or downloadable from the internet. This is convenient, but not entirely realistic, since
when you are working with your own data, you will have to gather, prepare, and then
input the data to the computer. The point of this first questionnaire on lateralisation is
therefore to give you a little practice at inputting data yourself. Later, datasets you are
given on other topics will emphasise analysis much more than data input). For now,
to get this data input process under way, follow these steps:
For SPSS 9 or lower
1. Start SPSS on your computer. (i.e. Start, Programs, SPSS for Windows). This
should open up the standard SPSS screen. Click Cancel if you are presented with
an inset screen showing previous files which have been opened. You should now
be confronted with a spreadsheet-type screen, with variables indicated at the top,
and then a blank screen consisting of rows and columns.
2. Go to the Data drop down menu, and choose Define Variable
3. On the screen that comes up, type in ‘ID’ in the box for Variable Name (replacing
the ‘Var0001’ which is probably being proposed).
4. There are four buttons in the lower part of the screen. Click on Type, and then,
from the radio buttons available, choose String. (Most of the time, the variable
type that SPSS guesses by default, i.e. Numeric, will be the right choice, but here,
it is as well to specify String, since typically, for ID, you will type in letter-based
identifiers.
5. Click Continue.
6. Back at the original Define Variable screen, click OK.
7. Notice that the name at the head of the column changes to ID.
8. Repeat this process for each of the remaining variables, giving each names such as
Sex, Q1, Q2. Each time, though, you should do exactly the same additional thing
for each variable:
- Click on the Missing Values button;
- Click on the Discrete Missing Values radio button;
- Type in ‘9’ in the left-hand box which becomes available;
- Click Continue;
- Click OK.
9. Note that because you are now dealing with numeric data, (for all variables except
ID), you don’t have to do anything with the Type button, since you can accept the
default choice that SPSS makes for you.

10. After you have finished defining and assigning a Missing Value code for the last
variable (Q11), go to the File drop-down menu, and save your work. You will
need to provide a name for the file you are saving. SPSS will automatically give
this file the suffix ‘.sav’, indicating that the file is a specialised SPSS file, saved in
a special format, and not available from other programs. In other words, you
cannot use a word processor to edit this data.
11. Input the actual data values for all six people, and for all fifteen variables, (i.e.
besides ID, put in data for sex, age, nationality and the eleven questionnaire
responses.)
12. Save the file
For SPSS 10 or higher:
1. Start SPSS on your computer (i.e. Start, Programs, SPSS for Windows). This
should open up the standard SPSS screen. Click Cancel if you are presented with
an inset screen showing previous files which have been opened. You should now
be confronted with a spreadsheet-type screen, with two tabs towards the bottom
left hand corner, labelled data view and variable view. You should be in the
variable view screen. If you are not, click on the variable view tab to move to it.
2. The top left hand box should be highlighted with a thick black line around it. (If
another box is highlighted, click on the top left hand corner box with the mouse.)
Type the name of the first variable, which in this case, is “ID”. Press ENTER.
3. Click on the data view tab, and notice that the title ID appears in the first column.
Then go back to variable view because we have not finished setting up this first
variable. (Most of the time you will actually be working with SPSS, e.g. when
you are actually analysing data, the variable view will be the one that is important.
Note in this view, columns are variables or measures and rows represent people.
But right at the beginning, when you have a new dataset, you need to tell SPSS
how you have set up your data. For this, data view is important. In other words, at
the beginning you spend a bit of time in this view, and then you leave it when you
do most of the analyses. Note also that, in contrast to the variable view, with data
view the ROWS represent variables, and the columns represent aspects of each
variable. So you have to make a mental adjustment about the orientation used by
each of these two screens when you move between them.
4. Click in the cell immediately to the right of ID. You will see a grey button in the
right hand part of this cell. Click it and a menu will pop up containing a number of
radio buttons. You use these to indicate what kind of data goes into this column.
In this case, choose string, as the ID will be a letter identifying each questionnaire
respondent. For all the other variables or measures in this dataset, the value will
be a number, and the appropriate choice will be numeric. Click OK to close the
dialogue box.
5. Click on the next cell to the right, below width. Click on the up and down arrows
to increase the column width to 10. You can leave the rest of the cells in the ID
row alone.
6. Still in variable view, click in the cell below ID. Type the name of the next
variable, SEX, and then set the type as numeric, and width as 10. Decimals should
be set as zero, as all the values for sex are whole numbers with no decimals.
7. In the cell below missing click on the grey button to open the dialogue box. Then
click on the discrete missing values variables button and type in the number ‘9’.

This means that where you have no information about the sex of a respondent, the
computer will give it a value of 9.
8. Set up the remaining variables in the same way (age, nationality, family
handedness, Q1, Q2,….Q11). In all cases the missing value code is 9, and the
decimal value should be set as 0, except for family handedness. As you can see,
this data goes to 2 decimal places, so change the decimals cell in this row to 2. A
major additional point: variable names in SPSS are limited to a length of eight
characters, so (a) you can’t use a name longer than this, and (b) you need to think
about how you “pack meaning” into the eight characters. Age is obviously easy,
but you might need to do things like use the name Fam_Hand so that the “real”
name of Family Handedness is transparent. Note, in passing, that the underscore
character is used here to separate the two “words”. SPSS doesn’t accept spaces in
variable names, and the underscores “fools” it, while preserving legibility. (We
will return to naming and labelling issues later in the chapter. Although restricting
names in the datasheet to eight characters is irritating, SPSS offers an excellent
solution to this, which we will cover later in the chapter, around pp 21-22.)
9. After you have finished defining and assigning a Missing Value code for the last
variable, go to the file drop down menu, and save your work. You will need to
provide a name for the file you are saving. SPSS will automatically give the file
the suffix “.sav”, indicating that the file is a specialised SPSS file, saved in a
special format and not available from other programs. In other words, you cannot
use a word processor to edit this data.
10. Now click on the data view tab and type the letters a-f in the left hand column, in
the six cells below ID. Input the actual values for all 6 people and for all fifteen
variables (i.e. besides ID, put in data for sex, age, nationality and the responses to
each of the eleven questions).
11. Save the file.
Reflection
You have done a set of specific things with the lateralisation dataset and obtained a
set of results. But what you have learned has general applications. These are that:
• you have learned how to give names to variables
• you have learned how to select either string or numeric data types
• you have learned how to specify missing values
• you have learned how to input data
And, more broadly, but very, very importantly, you have started to learn how to
structure a dataset.
O.K. you haven’t done anything with the data yet! But the data handling, defining,
and inputting skills will underlie everything that you do with SPSS.
Task 1.3
This is really a repeat of Task 1.1, simply reminding you about the data gathering that
you need to do.

Print out the actual questionnaire on lateralisation, make copies and give it to about
thirty people, trying, as you do so, to obtain as much variety as you can for sex, age
and nationality. When you have got this data, re-open the lateralisation data file, and
add the new data to it.
At the end of Chapter Three, you will be asked to do some work with this expanded
data file. Most of the data analysis in this chapter will use a different questionnaire
(the BIELT questionnaire) and associated data that will be given you so that you can
be set known tasks and given feedback on them. But at this later point, when you are
completing Chapter Three, you will also be asked to work with your own
(lateralisation) data, so that you can undertake a slightly less structured exploration.
The Main Questionnaire for this chapter: The BIELT Survey
The next section gives you some background information about the BIELT
questionnaire. It will help to contextualise the tasks which you will soon be doing.
In this section, I provide for you a brief description of a questionnaire survey which
was conducted jointly by BATQI (the British Association of TESOL Qualifying
Institutions) and the British Council into attitudes regarding the formation of a British
Institute of English Language Teaching (henceforth, BIELT). The data from this
survey, which is based on almost 1200 cases, is useful as a device to help learn about
the basics of questionnaire design. But it will make much more sense to you if you
first work through some background information so that you are familiar with the
issues involved.
BATQI1
, set up in 1992, has been concerned with professionalism and qualifications
within the English as a Foreign Language area of English Language Teaching. It has a
scheme which accredits courses leading to qualifications for teachers of ESOL
(English for Speakers of Other Languages). But the BATQI organisation, ever since
its inception, has also wanted to explore whether the English Language Teaching
profession would want to see established a general professional organisation, of the
sort represented in other fields by, say, the British Psychological Society, or the
British Medical Association, or the Institute of Mechanical Engineers. Such a new
organisation would be intended to inject a greater level of professionalism into ELT,
perhaps introduce a membership scheme with entry based on appropriate
qualifications which would convey to outsiders what levels of expertise exists, and
also perhaps act as a voice for the ELT profession. Discussions took place with the
British Council in 1998, and they agreed to finance a survey to explore what those
within the ELT profession thought of this idea.
The questionnaire was written by Charles Lowe, a member of the BATQI Committee
at that time, and sought to obtain information about a whole range of relevant areas.
Here are some sections of the questionnaire – to give you a flavour of how it worked
1
This organisation still exists, but is now known as QUITE.

(the full questionnaire can be found in Appendix One: note that you are only looking
at excerpts here):

Excerpts from the BIELT Questionnaire
FOR EACH STATEMENT, PLEASE CIRCLE THE NUMBER WHICH
REPRESENTS YOUR VIEW
Strongly Disagree Strongly Agree
1. A new body to represent the British ELT profession is necessary 1 2 3 4 5 6
2. Please add any further assumptions you would like included in the above list.
a. ______________________________________________________________________________
______________________________________________________________________________
b. ______________________________________________________________________________
Strongly
agree
Strongly
disagree
FOR THE PROFESSION, IT SHOULD:
3. Act as the sovereign and constitutive body – overarching and ‘supra’ 1 2 3 4 5 6
4. Establish an accepted framework of professional qualifications 1 2 3 4 5 6
5. Establish international equivalencies for qualifications 1 2 3 4 5 6
6. Establish a Professional Code of Practice for the protection of the
public
1 2 3 4 5 6
7. Function as a lobbying and public relations force for British ELT 1 2 3 4 5 6
8. Form official links (eg DfEE, EU, TTA, QCA) 1 2 3 4 5 6
9. Other __________________________________________________________________________
FOR ENQUIRERS AND NEW ENTRANTS, IT SHOULD OFFER:
33. A source of information about the profession 1 2 3 4 5 6
34. ‘Buyer beware’ advertisements (e.g. in the Guardian) 1 2 3 4 5 6
36. The baseline qualification for entry should be:
a Camb/RSA/Trinity Certificate (or similar) 1 2 3 4 5 6
b Camb/RSA/Trinity Diploma (or similar) 1 2 3 4 5 6
57. Role
___________________________________________
58. Gender aM bF
59. Age a20-25 e41-45
b26-30 f46-50
c31-35 g51-55
d36-40 h56-65
We will look at the questionnaire in more detail later, so for now you only need to
note a couple of points from these excerpts:
• some questions require pre-defined responses, i.e. 1, 3-8, 33-34, 36a, b, 58, 59
while others ask for more open information, i.e. 2, 9, 57

• most of the pre-defined questions use the 1-6 scale, but two (58,59) use other
categories, but ones which are not difficult to re-code numerically (as 1 and 2, and
1-8, respectively
• some of the open items generate data which was impossible to code easily, and so
were left out of the present dataset
• one of the open items (57: Role) was coded, since the responses, besides being
useful and important, did not fall into limitless categories. We will discuss this
more later.
The excerpts shown pretty much cover the sorts of items contained in the entire
questionnaire.
Through the offices of the British Council it was possible to distribute this
questionnaire world-wide, and on a very large scale. The responses to the
questionnaire were then coded and input to the computer by staff at the Centre for
British Teachers, (CfBT), and the data was sent to the author for analysis. You now
will have this dataset (or rather, a very large proportion of it), and the remainder of
the chapter requires you to look at this data. Simultaneously you will see what the
data reveals about the views of the British-oriented ELT profession regarding the
formation of a professional organisation, and you will also learn how to analyse
questionnaire derived datasets of this sort.
The Nature of the Questionnaire
If would probably be helpful if you located the complete questionnaire in the
Appendix so that you can refer to it while you are reading this section.
The point of sending people in the ELT profession a questionnaire was to establish a
range of things:
• most importantly of all, did they think the establishment of a professional
organisation was a worthwhile thing?
Assuming that the answer to this was positive, it was important to know therefore
what views they had about a number of options regarding such an organisation, such
as:
• What it should do:
• for the profession (Questions 3-9)
• for individuals (Questions 10-17)
• for schools/universities (Questions 18-22)
• for examination boards (Questions 23-25)
• for freelancers (Questions 31-32)
• for new entrants to the profession (Questions 33-34)
• Membership and qualifications issues (Questions 36-42)
• What its annual fee should be (Questions 44, 46)
• How it should come into being (Question 48)
In addition, it was felt important to gather information about respondents relating to
their location, age, gender, role, experience, area of work, qualifications, and salary.
(Questions 50 – 64). These biographical/background issues were probed through
fairly direct, factual questions. Some used simple choices (Male/Female for gender),
and others used more elaborate sets of options (e.g. for qualifications). But the entire

biographical section used fairly transparent methods, and simply depended on the
honesty and comprehensiveness of the answers given.
The sections on what a British Institute should do, how its membership systems
should function, etc (i.e. most of the questions 1-42) were framed in terms of six step
rating scales, with the arrangement:
Strongly Strongly
Disagree Agree
1 2 3 4 5 6
In this way, a gradation of views was possible, in which, interestingly, there was no
mid-point.
In these materials, I am treating this questionnaire as unproblematic in nature. By this
I do not mean that the design of the questionnaire cannot be criticised, or that there
weren’t other choices that could have been made as to how information was obtained,
or that there weren’t areas omitted. What I mean is that the measuring methods used
were straightforward. Questions were asked, and I am assuming the questions were
framed clearly, and that respondents understood what was meant, and provided the
relevant information in a direct manner. As a result, we can proceed directly to
analysing the data from the questionnaire. There may be times when these
assumptions cannot be made about questionnaires, but this doesn’t fit in with the
problem at hand, (learning how to analyse questionnaires), so it’s being ignored here.
The purpose of the pages which follow in this chapter, therefore, is simply to acquaint
you with processing data from a relatively straightforward questionnaire with a
reasonable number of questions, and a fairly large number of responses.
The guiding questions are:
• what questions can be addressed with this information?
• how can the information be presented in numerical form?
• how can the information be displayed?
Preparing the Questionnaire
Assuming that by now you have looked over the questionnaire in the appendix, you
will see what sort of information was returned with it. The next problem, for me as
data analyst, was to get the information from the questionnaires into an SPSS format.
It is useful in that regard to look at the following categories of data:
• the numeric items: many of the questions required six-step ratings. Putting these
into the computer was easy. Once a variable was named (i.e. Data, Define
Variable, and then the name was typed in with SPSS Version 9, or simply
completing the appropriate row in Data View in SPSS Version 10), it was a
simple matter to input the appropriate number which had been circled or crossed
by each respondent. Simple, but for the coders at the CFBT, laborious and time-
consuming, since there were a lot of data points per person, and then nearly 1200
respondents! Note that for these six steps, a missing value was defined as ‘9’.

• the “multiple choice” items which form a scale: many of the items required
choice from amongst a group of pre-defined categories. For example, the
question on the annual individual membership fee gave a series of amounts, and
required the choice of one. To cope with this, the following arrangement was
used:
a: £30 1
b: £40 2
c: £50 3
d: £60 4
e: £80 5
f: £100 6
Missing Value 9
The transformation of £30 to 1 and then the way the other values follow is
reasonably obvious in this case. The same applies for a number of items on the
last page of the questionnaire. The following codings were used:
Gender
Male 1
Female 2
Missing Value 9
Age
20 – 25 1 41 – 45 5
26 – 30 2 46 – 50 6
31 – 35 3 51 – 55 7
36 – 40 4 56 – 65 8
Missing Value 9
Experience (in years)
Language Teaching Teacher Training Materials/Publishing
0 –1 1 0 – 1 1 0 – 1 1
2 – 3 2 2 – 5 2 2 – 3 2
4 – 5 3 6 – 10 3 4 – 5 3
6 – 10 4 10+ 4 5+ 4
10+ 5
Research Management Exam Board
0 – 2 1 0 – 1 1 0 – 1 1
3 – 5 2 2 –5 2 2 – 5 2
6+ 3 6+ 3 6+ 3
Missing Value 9 for all sub-categories of experience
Employed/Freelance
Employed 1 Freelance 2 Both 3
Missing Value 9
Salary

£10-15k 1 £26-30K 4
£16-20k 2 £31-35k 5
£21-25k 3 £36k 6
Reflection
Notice, in all these cases, that the number of options available to respondents (and so
the codings that are possible) reflect the importance the questionnaire writer attached
to each of them and the number of sub-divisions he thought it would be meaningful
to make. Hence the three categories for Experience: Exam Board, but the five
categories for Experience: Language Teaching. In questionnaires you may use in the
future, you will have to think about the appropriate numbers of coding categories
when you need to deal with data of this sort.
• the complicated items: In addition to the above sorts of coding problems that the
questionnaire contains, there are some other items which require decisions. We
will take these one-by-one:
Reflection
Before we can really address the question of what to do here, we have to stand back a
little and reflect upon what we are trying to achieve. The relevance of this section is
general, and applies to pretty much any research-linked coding that one may do.
The key issue is that the codes that we choose now will be the organising basis for
analyses that we can do later. Think back to the salary codings from the previous
page. In this case there were six codings of salary. Because we coded salary in this
way, we can analyse all the other data in the questionnaire organised by salary. So,
for example, the responses to the question:
Q1: A new body to represent the British ELT profession is necessary
1 2 3 4 5 6
received the following average ratings (where the maximum possible rating was ‘6’),
(organised by salary level):
£10-15 5.23
£16-20 5.21
£21-25 4.44
£26-30 5.00
£31-35 4.60
£36+ 4.88

In other words, because we had the salary codings, we could “ask” the computer to
calculate the average ratings for Question 1, of the people who earned £10-15k; and
the average rating for Q1 of people who earned £16-20k, and so on. The codings for
salary, in other words, became the organising frame for reporting how these
subgroups gave ratings for Question 1. When we look at the actual numbers here, we
can see that all of these figures are fairly high (from a maximum of 6) but it is
interesting that the highest two ratings are associated with those who receive the
lowest salaries, and that the people with the highest salaries give clearly lower
ratings about how much they think such a new body is necessary.
The point here is that such a tabulation of information, in terms of mean scores, is
only possible because the data was coded to capture these six levels of salary.
Now we can restate the problem of how we code the information about country and
the other, codable, open questions. We need to code country in such a way that the
coding will allow us, later, to do the analyses that we want to do. And we have to bear
in mind that we may not, at the time of coding, be sure which analyses we will want
to do! Or, to put this another way, we need to make a decision about fineness of
coding. The fewer coding categories we use, the “cruder” the analyses of the data that
will be possible, but the easier it will be. The larger the number of coding categories,
the more detailed the potential analysis, but the greater the scope for confusion, also.
What you do in any particular circumstance has, therefore, to be a case-by-case
decision. Here are the decisions that were made in the present case:
• Country: Respondents actually provided the name of their specific country, e.g.
U.K, the Czech Republic, China. This presents two problems. First, while it
might be useful to be able to analyse all the Czech Republic respondents as a
group, there are, basically, too many countries in the world for it to be feasible to
give them each a separate coding number. Second, if we did have a specific
coding for the Czech Repubic, we might find that there are meaninglessly few
respondents who fall into that category. To handle these issues realistically, the
coding which was used was:
U.K. 1
North America 2
Australasia 3
Rest of Europe 4
Indian Sub-Continent 5
Far East 6
Middle East 7
Africa 8
Central/South America 9
Missing Value 99

• Qualifications: Question 62 offers, as options, a number of qualifications that
respondents might hold. The simple solution here is to code the different
qualifications sequentially, as has been done in this case. But it needs to be
realised that there is no straightforward scale at work here. Three of the
qualifications (Cambridge Diploma, Trinity Diploma, and a PGCE) give a
sufficient educational qualification, while the rest do not. Accordingly, a new
variable, Educational Qualification, has been created, which is coded ‘1’ for
anyone with one of these three qualifications, and ‘0’ for anyone who does not.
Notice that this variable exists in the SPSS data file, but did not exist in the
original questionnaire. In this way, it is possible to code, for the basic
qualification variable, the “highest” value achieved by what may be multiple ticks
from respondents while capturing separately whether there’s an actual educational
qualification. The interesting point to remember is that the original data can be
transformed, and operates as the starting point for further manipulation that is
useful
• Role: This question presents yet another set of problems. Since it is a completely
open question, respondents can answer in whatever way they choose. Even so,
there were frequently occurring responses. So, after some scanning of the
answers, the following (rather arbitrary) coding system was used for this question:
Tutor/Teacher 1 Consultant 11
Lecturer, EAP 2 Retired 12
FE College Lecturer 3 ESP 13
University Teacher 4 Employer 14
Coordinator/Organiser 5 EAL/ESL 15
Middle level post of responsibility 6 Admin/Non-professional 16
Senior level of responsibility 7 Inspector/Advisor 17
Manager 8 Examiner 18
Teacher Trainer 9 Student 19
Author/Writer/Editor 10 Missing Value 99
This may not be an ideal set of categorisations, but at a practical level, it served to
capture almost all of the open-ended responses that were made.
Note that the preceding discussion of the questionnaire items has not covered all the
items from the questionnaire – various of them have been omitted. This was because
the item concerned was too open-ended or unstructured to enable effective coding; or
because there were privacy issues involved; or because there was duplication. In any
case, there’s enough data to be going along with!

Task 1.4
In a moment you will be doing some analyses with the small version of the BIELT
questionnaire. Before that, though, there’s a small aspect of coding with SPSS that is
well worth learning, and the way to learn it is to use the dataset you are already familiar
with – that on lateralisation.
First, start SPSS (if its not already open), and open the Lateralisation dataset. As before
the data for the first six cases should look like this:
ID Sex Age Nat. Fam.
Han.
Q
1
Q
2
Q
3
Q
4
Q
5
Q
6
Q
7
Q
8
Q
9
Q1
0
Q1
1
a 1 2 2 0.00 1 1 1 1 1 1 1 2 1 1 1
b 1 2 4 0.08 1 1 1 1 1 1 1 1 1 1 1
c 2 3 3 0.15 1 1 1 1 1 1 2 9 1 3 2
d 2 2 6 0.32 2 2 2 2 2 2 2 2 2 2 1
e 1 1 6 0.10 1 1 1 1 1 1 1 1 1 1 1
f 2 3 4 0.18 1 1 1 2 2 1 2 2 1 1 2

What you need to do now is to make this datasheet less opaque, and as a result to make
it more accessible.
For SPSS Version 9
• Open the file, if it isn’t already open
• Click on the Sex variable heading and then choose Data, Define Variable
• Check that the missing value is set to ‘9’. If it isn’t, modify it so that ‘9’ functions
to signal missing values.
• Click on the Labels button, then
(a) Type ‘Sex’ into the Variable Label box
(b) Type ‘1’ into the Value box
(c) Type ‘Male’ into the Value Label box
(d) Click on the Add button (which has been dimmed ‘til this point) Notice that you are
building up a set of labels in the large box at the bottom of the screen.
(e) Repeat (b) and (c) with ‘2’ and ‘Female’
(f) Click Add again
(g) Click Continue
(h) Click OK
• You are now back at the main data screen. Locate the Value Labels button towards
the right end of the bar of icons almost at the top of the screen. While looking at the
data for Sex, click the Value Labels icon. Click it again. Interesting, eh? Once you
have added the value labels, clicking on this button allows you to toggle back and
forth between the data numbers and labels which refer to them.
For SPSS Version 10 or higher:
• Click on FILE, OPEN, DATA then choose the file in the place that you saved it.
Make sure you are in variable view
• In row 2, the row for sex, click the cell below the heading Label. Type in the word
sex
• In the same row, sex, click in the next cell to the right, in the column headed Values
a) Click on the grey button and a dialogue box opens
b) Type ‘1’ into the Value box
c) Type ‘male’ into the Value Label box
d) Click on the Add button (which has been dimmed up to this point). Notice that
you are building up a set of labels in the bottom part of the dialogue box.
e) Repeat (b) and (c) with ‘2’ and ‘Female’
f) Click Add again
g) Click OK
• You are now back at the main data screen. Click on the Data View tab. Now click
on the View drop down menu and click on Value Labels if it hasn’t already got a
tick next to it. Look at what happens to the data for sex. Interesting, eh? Click on
VIEW, VALUE LABELS again, and the data will go back to being in the form of 1’s
and 2’s. Once you have added the value labels, clicking on VIEW, VALUE LABELS
allows you to toggle back and forth between the data numbers and labels which
refer to them.

Reflection
Once again I have to say that you have learned a powerful technique. It’s tedious to
go through the Data, Define Variable sequence, and then attend to Missing Values
and Value Labels. But it’s very important. With Missing Values this importance is
obvious. With Value Labels it’s more subtle. But here are two crucial reasons. First,
it’s easy to forget! With Sex there are only two values, and they are fairly obvious, so
the only danger is getting them the wrong way round. But with a long and arbitrary
list like that for Role (covered on P20, above) it’s difficult to keep things in your head
and the capacity to switch quickly between labels and data is really useful. Second,
the labels you put in for each variable and value are what appear in the SPSS output.
This circumvents the 8-letter limit on variable names, and is immensely more
communicative for the different values, for a variable. It makes the difference between
clarity and incomprehensible output! So when you are inputting the original data, it’s
well worth the tedium of adding Label information, for each variable, as appropriate.
Task 1.5
Put in variable names and value labels for the rest of the variables on the
Lateralisation dataset. (Referring back to pp 5-6 might be helpful for this.) Then use
the relevant icon from the bar of icons (or VIEW, VALUE LABELS) to see the effect
of what you’ve done.
Hints:
1. You will need to do this task for every variable except Familial Handedness. (Of
course, SPSS will still work if you don’t do this, but the output you generate will
be much less clear, so it's worth putting up with the tedium at this stage.)
2. Note that with SPSS 10, working in Variable View, you could, for example, fill in
the values for Question One, and then copy this material and paste it into other
variables which have exactly the same labels.
3. The overriding purpose in this task is to input data which will help you, with the
meaningfulness of output, and help readers of that output. You are no longer, at
this stage, restricted to eight characters, but even so, don’t be too verbose! With
Variable Name you can be a little more descriptive, but with Value Labels, try to
be concise. Note also you can use proper “spaces” at this stage!

Appendix One: The Main BIELT Questionnaire
The ‘British Institute for ELT’
A survey of ELT Professionals
May 1997
Instructions
This questionnaire is being sent to professionals at all levels and in all sections
and is the biggest consultation exercise in ELT history. To make it effective, we
depend on getting a maximum possible response.
It consists of 64 questions and should take about 15 minutes to complete.
If you are a chief executive, head of dept, or responsible for a group of
staff, please can you make sure that everybody at your place of work gets
one (please copy extras as necessary), and that they return it, filled in, as
soon as possible, so that you can send it to the address below. Except in
special circumstances, freepost envelopes should have been provided for
your convenience (one per questionnaire).
If you’re answering for yourself alone, please fill it in and send it back to
the address below. Where practicable, a freepost envelope has been
provided for your convenience.
The deadline for receiving returns is Saturday June 28, but in certain
circumstances this is being extended.
The deadline for receiving returns is Saturday June 28, but in certain
circumstances this is being extended.
THANK YOU VERY MUCH FOR YOUR TIME AND ATTENTION. IT IS
MUCH APPRECIATED.
Coordinator, BI Steering Group, 23 Heythorp Street,
London, SW18 5BW
Great Britain

The “British Institute for English Language Teaching”
A survey of ELT Professionals – May 1997
Dear Colleague,
Introduction
The British Institute Steering Group is made up of one representative from each of
the main ELT bodies in the UK, namely: ARELS, BAAL, BALEAP, BASELT,
BATQI, the British Council, the College of Preceptors, FIRST, the Freelancers,
IATEFL, IELTDHE, NALDIC, NATECLA, Publishers’ Association (ELT Section),
Trinity College London, UCLES. (Also invited have been: GMB, ESU, LCCI, Exam
Board, and SATEFL).
After two conferences, several Steering Group meetings, and an IATEFL
presentation, we are firmly on the way. A British Institute for ELT within the next 18
months is now a real possibility. In order to go forward, we want now to consult the
widest possible range of professional opinion.
The survey questionnaire is therefore directed to everyone who works in British ELT
anywhere in the world, of any nationality, (i) who holds an accredited British ELT
qualification, and/or (ii) has contributed to British ELT overy many years. In the first
instance it is being sent to people connected with the member organisations with the
Steering Group, all of whom have agreed to its design and distribution. It has simply
not been possible systematically to organise sending it to those who are not so
connected. Such people are equally invited to participate. In other words the
questionnaire is for all those involved in the following.
Language teaching (EFL/ESL/EAP/ESP), language support, teacher training and
education, educational management, owning or running a school, running a large
organisation, authorship of materials, publishing, research, examining language,
examining teaching, freelance work, administration, university teaching. FE teaching,
and so on.
It is planned to reach approximately 7000 respondents. Because of the thoroughness
of our coveraage, you may receive the Questionnaire on more than one occasion from
different sources.

Background
Since the BATQI Conference of June 1996, moves have been under way towards a
unifying professional body for British ELT. With such a body, it is certain that the
landscape of British ELT will change. The implications for the position of British
ELT in the world will be considerable. A number of things make a new supra-
professional body an attractive idea at this defining stage in the development of the
ELT profession.
Procedure
This Questionnaire should take roughly FIFTEEN MINUTES to fill in. Most
questions require a multiple choice answer. Wherever a longer response is requested
we will value your comments and take them fully into account.
It goes without saying that your view is of extreme importance. The structure and
priorities of the new body will be shaped by your input.
For background information or explanations to queries you may have, please fax the
BI Steering Group Coordinator (fax +44 (0) 181 870 0012) and put your query.
The deadline for returning this Questionnaire is Saturday 31st
May (extended in
special circumstances) In most cases a pre-paid envelope will have been provided.
Data Protection: The information you put on this document of a personal and
identifying nature is protected by the Data Protection Act, and in any case will be
treated with the utmost confidentiality.
May we thank you very much in advance for your time and attention.
Yours sincerely
Bruce Napier (Chair, BI Steering Group)
Members of the BI Steering Group
April 1997
SECTION 1 – GENERAL BACKGROUND

In this section, we will outline some central ideas in our thinking so far, and then we want to hear
your general and ‘in principle, reactions to them.
In the many discussions that have already taken place, a consensus has been forming. It is
generally felt that the new organization would at the very least:
• be made up primarily of individuals. Institutions will be affiliated.
• be made up of professionals with acknowledged skills and qualifications. (‘qualified’ to be
defined.)
• promote unity of spirit and purpose across all sectors of British ELT.
• provide the basis for a new improved image of British ELT outside the UK, and hence a
coordinated and focused response to competition from other ELT nations.
• be dedicated to raising the standards of teaching and training in the British ELT context
around the world.
• offer to its individual members the benefits of status and special services.
• speak for the whole profession and lobby the government on crucial issues.
o o o o o o o o
FOR EACH STATEMENT, PLEASE CIRCLE THE NUMBER WHICH
REPRESENTS YOUR VIEW
1. A new body to represent the British ELT profession is necessary 1 2 3 4 5 6
2. Please add any further assumptions you would like included in the above list.
a. _______________________________________________________________________
_______________________________________________________________________
b. _______________________________________________________________________
_________________________________________________________________
SECTION 2 – THE ROLES OF THE NEW BODY
In this section we explore in more detail the potential aims, roles and functions of the new body. In
a sense, everything depends on everything else. One decision on its ‘roles’ may determine another
decision on its ‘structure’. But this is only true up to a point, because certain priorities have
already suggested themselves and these have pushed us in a certain direction. For instance, we
think it would be first and foremost an organisation of individuals, which would for instance have
implications for its membership criteria, and thus how many members it would be likely to attract.
It must offer people something they feel they will benefit from. At the same time, it has to be
effective in bringing coherence to the profession.
o o o o o o o o
Assuming sufficient funds, the new Institute could fulfil some of the following functions: (*

indicates this function is fulfilled by one or more existing organisations). Your response here will
help identify the priorities.
Strongly Strongly
disagree agree
FOR THE PROFESSION, IT SHOULD PROVIDE:
3. Act as the sovereign and constitutive body – overarching and ‘supra’ 1 2 3 4 5 6
4. Establish an accepted framework of professional qualifications 1 2 3 4 5 6
5. Establish international equivalencies for qualifications 1 2 3 4 5 6
6. Establish a Professional Code of Practice for the protection of the public 1 2 3 4 5 6
7. A lobbying and public relations force for British ELT 1 2 3 4 5 6
8. Form official links (eg DfEE, EU, TTA, QCA) 1 2 3 4 5 6
9. Other __________________________________________________________________________
FOR INDIVIDUALS, IT SHOULD PROVIDE:
10. Status ie letters after one’s 1 2 3 4 5 6
11. Career structure (routes, structures, opportunities) 1 2 3 4 5 6
13. A profession-wide annual conference* and journal* 1 2 3 4 5 6
14. Assistance with local teachers groups* 1 2 3 4 5 6
15. A central resource and library service 1 2 3 4 5 6
16. Reductions on book purchases etc. 1 2 3 4 5 6
17. Other __________________________________________________________________________
FOR SCHOOLS/UNIVERSITIES, IT SHOULD PROVIDE:
18. A comprehensive register of members 1 2 3 4 5 6
19. An accepted framework of qualifications 1 2 3 4 5 6
20. International equivalencies 1 2 3 4 5 6
21. Consultancy services* 1 2 3 4 5 6
22. Other _________________________________________________________________________
FOR EXAM BOARDS, IT SHOULD PROVIDE:
23. ‘Buyer beware’ advertisements (eg in the Guardian) 1 2 3 4 5 6
24. A framework of qualifications 1 2 3 4 5 6
25. Other __________________________________________________________________________
IT SHOULD SUPPORT THE DEV’T OF PROFESSIONAL KNOWLEDGE THROUGH:
26. Central and cross-sectoral research coordination 1 2 3 4 5 6
27. An action research journal 1 2 3 4 5 6
28. A professional journal* 1 2 3 4 5 6
29. Branches and specialised groups* 1 2 3 4 5 6
30. Other ___________________________________________________________________________
FOR FREELANCERS, IT SHOULD OFFER
31. Connection to a central coordinated professional resource 1 2 3 4 5 6
32. As above, as relevant 1 2 3 4 5 6
FOR ENQUIRERS AND NEW ENTRANTS, IT SHOULD OFFER:
34. ‘Buyer beware’ advertisements (eg in the Guardian) 1 2 3 4 5 6
35. List your TEN MOST IMPORTANT FUNCTIONS in order, by the reference numbers above:
a b c d e f g h i j

SECTION 3 – MEMBERSHIP ISSUES
We have assumed, as stated in Section 1, that membership will be in principle restricted to those
with acknowledged skills and qualifications. However, this is probably best considered as a
medium-term strategy. In other words, we would surely agree that we need to ‘sort out’ the
profession for those who are entering it now and who will be running it in 10 years time. For the
present, we need to cater not only for those who have acquired bona fide qualifications, but also for
those many who, having entered ELT 20 or so years ago and having since contributed greatly to it,
were not in a position to gain the kind of qualification we may nowadays take for granted as a
baseline, such as a Diploma level qualification (eg UCLES/Trinity etc.) We have referred to such
people as ‘seniors’.
There is also the issue of institutional membership. The Steering Group is made up of institutional
representatives. But will institutions have a central place in the new body?
And what of publishers, textbook authors, the owners of language schools, applied linguists,
language centre managers, and all those who manage and administer our testing and examination
systems – are they to be denied access to the new body? We have referred to such people as
‘cognate professionals’.
As usual, please state your views on these matters below.
Strongly Strongly
disagree agree
36. The baseline qualification for entry should be:
a Camb/RSA/Trinity Certificate (or similar) 1 2 3 4 5 6
b Camb/RSA/Trinity Diploma (or similar) 1 2 3 4 5 6
37. There should be different levels of membership for different qualifications
(eg associate member, full member, fellow, etc.) 1 2 3 4 5 6
38. It would be good if Full Member status could signal a genuinely significant
achievement in the profession (eg Diploma level qualification with at least
2000 hrs teaching.) 1 2 3 4 5 6
39. ‘Senior’ status would be an appropriate way of including older members as
‘founders’. 1 2 3 4 5 6
40. ‘Cognate members’ should be given:
a Affiliate status (see Institutional Members below) 1 2 3 4 5 6
b Member status (see Grandparent status above) 1 2 3 4 5 6
41. Institutions should be:
a active participating members each with at least one full vote equivalent
to that of a full member 1 2 3 4 5 6
b ‘affiliate members’ with a limited role 1 2 3 4 5 6
42. Please add further comments here
__________________________________________________________
___________________________________________________________________________________
SECTION 4 – STRUCTURE
In this section we want to hear your views on the suggestions for the structure of the new body.

Once past the transition stage, the general consensus in the Steering Group is that it will need a
governing council, made up of elected members and institutional representatives. The Chair (paid)
will be elected periodically, and will be a distinguished ambassador for British ELT. They will be
assisted by a small paid secretariat.
There will be a Committee devoted to membership issues, a framework of qualifications,
international equivalences and a Code of Practice. Other divisions will be concerned with (i) press,
PR and lobbying (ii) professional development (journals, conferences, networking, research, etc)
(iii) information collection and dissemination.
As for membership structure, there is a general view that it should offer levels of membership
within which there is a strong incentive to move to full membership and beyond.
43. I would add the following ideas to the structure proposed:
SECTION 5 – FINANCE
In this section we want to find out what people are prepared to pay for membership.
It is estimated there are approximately 5000 potential individual members around the world of
whom about 2000 could have joined by the year 2000. Please think about the value of this new
body to you and its value to the profession.
It is estimated there are approximately 500 potential institutional members around the world of
whom about 200 could have joined by the year 2000. Please think about the value of this new body
to your institution and its value to the profession. Note: It is widely agreed that institutional
membership will not confer individual membership, as it does for IATEFL. All individual members
would be asked to meet the accepted criteria on qualifications and/or experience.
44. Annual individual membership should be:
a£30 c£50 e£80
b£40 d£60 f£100
45. For this, individual members should expect as a minimum:
a_____________________________________________
b_____________________________________________
c_____________________________________________
46. Annual institutional membership should be:
a£300 c£500 e£800
b£400 d£600 f£1000
47. For this, institutional members should expect as a minimum:
a_________________________________________________
b_________________________________________________
c__________________________________________________

SECTION 6 – GETTING FROM HERE TO THERE
In this section we want to hear your comments on the transition period. We are suggesting four
alternatives.
Strongly Strongly
disagree agree
48. Please give your responses
a Start from scratch as a free-standing institution 1 2 3 4 5 6
b ‘Piggy Back’ on another organisation until ready to stand alone 1 2 3 4 5 6
c Become a section of another organisation 1 2 3 4 5 6
d Ask another organisation to become the British Institute 1 2 3 4 5 6
Comment ____________________________________________________________________
_____________________________________________________________________________
_____________________________________________________________________________
_______________________________________________________________________
_______________________________________________________________________
___
SECTION 7 – FURTHER COMMENTS
49. In this section we would welcome any further comments you would like to make.
___________________________________________________________________________________
___________________________________________________________________________________
___________________________________________________________________________________
___________________________________________________________________________________
___________________________________________________________________________________
___________________________________________________________________________________
___________________________________________________________________________________
___________________________________________________________________________________
___________________________________________________________________________________
___________________________________________________________________________________
___________________________________________________________________________________
___________________________________________________________________________________
___________________________________________________________________________________
SECTION 8 – SOME DETAILS ABOUT YOU
In this section we want to get some information about all of our resondents. Please be assured that
the completing of the questionnaire does not commit you in anyway to future membership of
anything. Your home-address details would be appreciated so that be can build a complete

professional mailing list.
50. a Mr / b Mrs / c Dr / d Prof
_________________________________
51. First Name
_________________________________
52. Surname
_________________________________
53. Contact Address
_________________________________
_________________________________
_________________________________
54. Telephone/Fax
_________________________________
55. Organisation/Workplace
_________________________________
56. Country
_________________________________
57. Role
_________________________________
58. Gender a M b F
59. Age a20-25 e41-45
b26-30 f46-50
c31-35 g51-55
d36-40 h56-65
Experience in ELT (yrs)
Language Teaching a0-1
b2-3
c4-5
d6-10
e10+
Teacher trng/educ’n f0-1
g2-5
h6-10
i10+
Materials/publish’g j0-1
k2-3
l4-5
m5+
Research n0-2
o3-5
p6+
Management q0-1
r2-5
s6+
Exam Board t0-1
u2-5
v6+
Other
___________________________________
61. Current Sector (tick any as relevant)
aCommercial ELT organisation
bFE ELT dept
cUniversity lang centre/EAP
dEFL State Primary or Secondary
eESL Adult
fESL Children
gPublishing
hAuthoring materials
iUniversity teacher education
jUCLES training
kTrinity College training
lConsultancy/contract training
mResearch
nOther
62. Qualifications (tick any as relevant)
aCambridge/RSA Cert
bCambridge/RSA Diploma
cTrinity Cert
dTrinity Licentiate Diploma
eOther Accredited Cert or Dip
_____________________
fPGCE in ELT in other? _________
gUniversity Postgrad Dip ELT
hMasters ELT
iMasters Applied Linguistics
jAlternative Profile
63. aEmployed bFreelance
64. Salary a10-15k d26-30k
b16-20k e31-35k
c21-25k f36+k
Comment _______________________

50. aMr / bMrs / cDr / dProf
_________________________________
51. First Name
_________________________________
52. Surname
_________________________________
53. Contact Address
_________________________________
_________________________________
_________________________________
54. Telephone/Fax
_________________________________
55. Organisation/Workplace
_________________________________
57. Country
_________________________________
57. Role
_________________________________
58. Gender aM bF
59. Age a20-25 e41-45
b26-30 f46-50
c31-35 g51-55
d36-40 h56-65
Experience in ELT (yrs)
Language Teaching a0-1
b2-3
c4-5
d6-10
e10+
Teacher trng/educ’n f0-1
g2-5
h6-10
i10+
Materials/publish’g j0-1
k2-3
l4-5
m5+
Research n0-2
o3-5
p6+
Management q0-1
r2-5
s6+
Exam Board t0-1
u2-5
v6+
Other ___________________________________
61. Current Sector (tick any as relevant)
aCommercial ELT organisation
bFE ELT dept
cUniversity lang centre/EAP
dEFL State Primary or Secondary
eESL Adult
fESL Children
gPublishing
hAuthoring materials
iUniversity teacher education
jUCLES training
kTrinity College training
lConsultancy/contract training
mResearch
nOther
62. Qualifications (tick any as relevant)
aCambridge/RSA Cert
bCambridge/RSA Diploma
cTrinity Cert
dTrinity Licentiate Diploma
eOther Accredited Cert or Dip
_____________________________
fPGCE in ELT in other? ____________
gUniversity Postgrad Dip ELT
hMasters ELT
iMasters Applied Linguistics
jAlternative Profile
63. aEmployed bFreelance
64. Salary a10-15k d26-30k
b16-20k e31-35k
c21-25k f36+k
Comment _______________________

Spss basics

Recommended

Recommended

More Related Content

Similar to Spss basics

Similar to Spss basics (20)

More from Kritika Jain

More from Kritika Jain (20)

Recently uploaded

Recently uploaded (20)

Spss basics