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SPSS is a widely used statistical package in social science. Many of you will have used it before. This worksheet gives the basics for those who have not, using version 15 of `SPSS`

(the computers will regularly be updated, but the commands should mostly be the same...).

- Starting SPSS in room G37
- SPSS Windows, menu & toolbar
- Data set: Workers Survey
- Entering data into SPSS
- Adding variable labels & value labels
- Defining missing values
- Other variable characteristics
- Saving data
- Descriptive statistics. Frequencies: Discrete variables & bar charts
- Output & data windows
- Frequencies for true numeric variables: Summary statistics & histograms
- Compare means: Means for subgroups
- Scatterplots & testing correlations
- Editing & saving the output, leaving SPSS
- Restarting SPSS, retrieving previous data & output files
- The SPSS Help system, online help, & books

- Starting SPSS in Room G37
- SPSS windows, menus, & toolbar
`File`

: Create & open files (either`SPSS`

files or from other software packages)
Files may be data, output, syntax, or chart files. Also use this menu to exit from `Edit`

: Modify, copy, cut, paste text, insert variables, etc`View`

: Display toolbars & status bar, change fonts, & display gridlines & value labels`Data`

: Sorting, merging, selecting, & weighting cases in the current data file`Transform`

: Change selected variables, recode & compute new variables`Analyze`

: Select statistical procedures to analyse your data`Direct Marketing`

: huh?`Graphs`

: Create a wide range of graphs from your data`Utilities`

: Display variables, list variable information, define sets of variables for analysis, run scripts, & edit menus`Add-ons`

Help IBM make more money from SPSS products`Window`

: Move between, arrange, select, & control the various`SPSS`

windows`Help`

: Get help about any feature within`SPSS`

Context sensitive help is available through the dialogue boxes. The - Data set: Workers Survey
- Interval/Ratio
- Ordinal
- Categorical/Nominal
- Discrete
- Continuous
- Entering data into SPSS
- Adding variable labels & value labels
- Click in the
`Value`

box - Type 1
- Click in the
`Value Label`

text box, type "White" - Click on the
`Add`

button - Defining missing values
- Select the
`Missing`

cell for variable`daysabs`

& click the button - Click the
`Discrete missing values`

radio button - Type
**999**in the first discrete missing value box - Click
`OK`

- Other variable characteristics
- Saving data
- Click on
`File`

|`Save`

or`Save As`

- The keyboard short-cuts are (usually):
`Ctrl+S`

(for`Save`

) &`Ctrl+Shift+S`

(for`Save As`

) `SPSS`

data files have the default extension`.sav`

- Descriptive statistics. Frequencies: Discrete variables & bar charts
- Output & data windows
- Frequencies for true numeric variables: Summary statistics & histograms
- Compare means: Means (etc.) for subgroups
- Scatterplots & testing correlations
- Editing & saving output, leaving SPSS
- Restarting SPSS, retrieving previous data & outputs
- The SPSS help system, online help, & books
- Field A (2009) Discovering statistics using
`SPSS`

, Sage, 3rd edition
Very comprehensive; it is based on PASW v17, but fine for other versions. There is lots of additional - Dancey CP, Reidy J (2007) Statistics without maths for Psychology, Pearson/Prentice Hall, 4th edition Simpler than Field. Chapter 2 introduces
- Kinnear PR, Gray CD (2007)
`SPSS`

15 made simple. Psychology Press.
Good for beginners. Much better than earlier editions of the same book. It links - Green SB, Salkind NJ (2007) Using
`SPSS`

for Windows & Macintosh, Prentice-Hall (Pearson) 5th edition
Also for beginners. Includes many of the techniques used in our postgraduate statistics course.

`SPSS`

has different-numbered versions. For this course, we will use `whatever version is installed!`

. The computers in G37 have at least one version & may have some older versions as well. All the versions work similarly, & you should easily be able to use an earlier version once you know the latest one. Be aware that the different versions all use the same kind of data file (& syntax file, which you'll learn about in week 3) but they cannot read each others' output files. This is not a problem because you can transfer your work between different versions using data & syntax files.

To open `SPSS,`

, log on to `Windows`

, click the big round button `Start`

| `All Programs`

| `SPSS Inc`

| `PASW Statistics XX`

| `PASW Statistics XX`

.

You'll see a starting box `What would you like to do?`

with various options. You will be entering new data, not reading from a file, so select `Type in data`

, click `OK`

& the `SPSS Data Editor`

screen opens.

If you have never used `SPSS`

(or have, but want to refresh your memory) it is worth looking over the elements of the `SPSS`

screen & menus before using it. If you are experienced with `SPSS`

, go straight to section 3.

When you start `SPSS`

, the first window that is opened for you is the `Data Editor`

: A spreadsheet-like window for defining & entering data. This also includes the `Application Window`

, which has a menu bar & tool bar at the top, underneath the title bar, & a status bar along the bottom. Other windows may be opened for you as appropriate during processing, e.g., an `Output`

window, `Syntax`

, or `Chart`

windows.

`SPSS`

is *menu driven*; that is, most of the features can be accessed by selections from the menus. The main menu bar contains ten items, as follows:

`SPSS`

`SPSS`

Help is varied & extensive - you will find some guidance on it in the last section of this handout.
Look at each menu in turn. Note which items appear under each menu. The **items listed in bold** are those available or appropriate at this point.

`SPSS`

for Windows Tool BarThe tool bar underneath the menu bar allows you to access many of the most commonly used features. To find out what each of the options along the tool bar does, place the cursor over the tool for a moment & look at the pop-up box, where a short description will be displayed.

`SPSS`

SPSS is an extensive system & you will often want to find help or information that is not in my handouts. The last section of this handout gives guidance on using online help, & recommends some books.

The table below shows part of the results of a survey about workers. Each row (`case`

) contains information (seven variables) about a single person. The variables & their values are defined below.

subj | ethnicgp | sex | yrs | satis1 | satis2 | daysabs |
---|---|---|---|---|---|---|

1 | 1 | 1 | 1 | 1 | 4 | 8 |

2 | 2 | 1 | 5 | 2 | 2 | 9 |

3 | 3 | 1 | 5 | 1 | 1 | 7 |

4 | 3 | 1 | 15 | 2 | 2 | 4 |

5 | 2 | 2 | 36 | 4 | 3 | 0 |

6 | 0 | 1 | 31 | 3 | 3 | 2 |

7 | 1 | 1 | 2 | 0 | 3 | 1 |

8 | 4 | 1 | 35 | 4 | 4 | 3 |

9 | 1 | 2 | 1 | 4 | 4 | 10 |

10 | 1 | 1 | 10 | 4 | 5 | 999 |

11 | 2 | 2 | 7 | 3 | 3 | 4 |

12 | 2 | 2 | 9 | 3 | 5 | 3 |

Type | ? | ? | ? | ? | ? | ? |

? | ? | ? | ? | ? | ? |

Which type of variables are these?

And, are these variables?

In `SPSS`

, you define variables & enter data in the `Data Editor`

. The `Data Editor`

is a window that looks like a spreadsheet, with an array of cells in which you enter data. Unlike a spreadsheet, you can only enter data, not formulae.

The empty `Data Editor`

is illustrated below.

The `Data Editor`

has two views, `Data View`

& `Variable View`

(see tabs at bottom left of screen). The illustration below shows the `Data View`

.

This empty window is called `Untitled1 [Dataset0]`

. Versions 14 onwards of `SPSS`

allow you to have more than one `Data Editor`

window open (`Dataset0`

, `Dataset1`

, etc).

In the `Data View`

, *rows* are `cases`

(a participant or other unit of observation ), & *columns* are `variables`

. The variables are unnamed by default, labelled simply `var`

. The `Workers Survey data`

have seven variables, so you will use seven columns, naming them after each variable `subj`

to `daysabs`

.

The `Variable View`

of the `Data Editor`

is where you define the characteristics of your variables. It is possible to enter data without naming variables, but it is better, & avoids errors, if you start defining the variables first. To do so, click the `Variable View`

tab at bottom left, & you see this:

In the `Variable View`

, each *row* corresponds to a variable. In each row you can define the name, type, & other characteristics of the variable.

Type the names of the first two variables from the data set, `subj`

& `ethnicgp`

, in the first two rows under `Name`

. Variable names can be in lowercase or capitals (lowercase is preferable). We have used variable names that are short, no more than eight characters. After each variable name, press `Enter`

. The screen will now look as follows:

`SPSS`

inserts the default variable characteristics each time you type in a new variable name. By default, it assumes variables will be of the type `numeric`

, have a maximum `Width`

of eight digits including decimals, & two decimal places. Other parameters are undefined by default.

For the moment, return to `Data View`

, & you will see the two new variable names at the top of the first two columns.

Type (or `Copy`

| `Paste`

) in the `subj`

& `ethnicgp`

data from the 12 subjects, copying from the table above. To correct any errors, select the cell & re-type. `SPSS`

automatically gives the data two decimal places, & allows up to eight digits, as specified in the variable characteristics.

You can leave the `Width`

& `Decimal Places`

as they are, but it's neater to change them. Return to `Variable View`

& select the `Type`

cell for either of the variables (in the illustration above it's selected for `ethnicgp`

). A small button appears at the right-hand end of the cell as shown. Click this & you see a `Variable Type`

dialogue box:

Change the number of `Decimal Places`

to zero. Change the `Width`

to the maximum number of digits needed for values `ethnicgp`

. Click `OK`

. Repeat the process for the variable `subj`

.

You see that this box allows you to do other things, e.g., change variables from `numeric`

to other types (note that `numeric`

here does not imply that the data are interval data...). We recommend that you generally make your variables numeric, because `SPSS`

statistical procedures don't always work well with other types.

Return to `Data View`

to see the effect of the changes.

Before entering more data, go back to `Variable View`

, type in the names of the remaining variables (`sex`

, etc.), & define the `Width`

& `Decimal Places`

. Instead of the `Type`

, you can click on `Width`

& `Decimal`

& change the numbers there. (For any continuous variables, leave `Decimals`

set to two rather than changing it to zero.).

`SPSS`

allows you to label your variables, which often makes the output of analyses easier to understand. There are two types: `Variable labels`

, & `Value labels`

.

A `Variable label`

is simply a more detailed description of what that variable is (e.g., for the variable `yrs`

you might wish to add the variable label `Number of years working for this company`

. (Alternatively, you can use longer variable names, but these can become difficult to work with).

`Value labels`

are often helpful for a variable which has a small number of values (a *discrete* variable); for example `ethnicgp`

, whose values are 1, 2, 3, & 4. By adding value labels, you record that value "1" has label "White", value "2" has label "Asian", etc.

Go to `Variable View`

. If you want to provide a `Variable label`

for `yrs`

, click in the `Label`

column opposite this variable, type in the description & press `Enter`

.

`Value labels`

, as noted above, are often used for discrete variables, especially where it is not obvious what the values stand for. These may be categorical or ordinal variables.

To define value labels for `ethnicgp`

, click in the `Values`

cell of that variable, & a button appears (as shown below). Click the button to obtain the `Value Labels`

dialogue box.

To add the first `Value Label`

:

And add the rest of the value labels...

**Note:** Value 0 indicates a subject whose ethnic group is *not known*. Such subjects must be omitted from any analyses that use the variable `ethnicgp`

. In the next section you will define 0 as a `Missing Value`

for `ethnicgp`

. So it is not essential to provide a label for value 0, although it may be helpful. Occasionally, it is important to label missing values, e.g., when there is more than one type of missing value.

When you have labelled all the values you want to, click `OK`

.

Repeat the labelling procedure for any other variables in the data set that you wish. They do not all require `Value Labels`

.

**Shortcut:** The 2 `satis`

variables have identical value labels. To save unnecessary typing, you can define the `Value Labels`

for one of them, & `Copy`

these for the other one. Define the labels for `satis1`

, then select its `Labels`

cell, click `Edit`

| `Copy`

on the menu, select the `Labels`

cell for `satis2`

, & click `Edit`

| `Paste`

. The same labels will be inserted.

After you have defined `Value Labels`

, you can make `SPSS`

display either the labels or the numeric values in the `Data Editor`

. To control this (& to toggle on/off), click on `View`

| `Value Labels`

.

**It is essential** to tell `SPSS`

what values of a variable correspond to **missing data**, e.g., where data were lost, or a participant refused to reply. This will ensure that `SPSS`

omits such cases from any analyses that use that variable. If it tried to include them, the analysis would not make sense.

`SPSS`

has a standard missing value code (.) for numeric data values that are missing: This is known as a `System Missing`

value. However, it is often helpful to specify `user missing values`

, as follows.

Looking at the variable descriptions in Section 3, you see that, for many variables (e.g., `ethnicgp`

, **0** signifies a missing value: `Not Known`

), but, for one variable (`daysabs`

), a special value **999** signifies missing values. A value of 0 would not be appropriate because it's a possible real value: A person could be absent from work for 0 days in a year. To convey this information to `SPSS`

: In `Variable View`

, click on the `Missing`

cell for the variable & click the button to produce the `Missing Values`

dialogue box.

This provides options for defining missing values. By default, `SPSS`

treats all values as valid (not missing) so the initial setting is `No missing values`

. You can declare up to three separate, discrete values, & specify these as missing, or a range of missing values. `SPSS`

allows up to three missing values because, e.g., in survey research there might be different reasons for missing values (e.g., *refused*, *not applicable*, *don't know*), & you might want to distinguish these. All of these responses can be given different numeric codes, & `Value Labels`

, & then defined as missing values, allowing these responses to be treated differently in the subsequent analysis.

In the `Workers Survey`

data set, some variables have **0** as the missing-value or a `No Response`

code, & `daysabs`

has **999** for missing values.

To specify this as a missing value to `SPSS`

:

Specify **0** as a missing value for `ethnicgp`

, using the above procedure.

Some other variables have 0 specified as a missing value, so insert these as well. Be careful not to use **0** as a missing value if it is a possible real value for that variable

**It is good practice to define suitable missing values for all variables**. (There may not be missing data among these 12 subjects, but you might add other subjects later who do have missing data.) So follow the equivalent steps for all the variables in the data set.

**Displaying Variable Information**. To check the characteristics of any of the variables on your file, look at the `Variable View`

screen, or go to `Data View`

, select `Utilities`

| `Variables`

. This displays a dialogue box of all the variables you have specified. To choose one of the variables, click on the variable name on the left hand side of the dialogue box, so that it is highlighted. Information about that variable is then displayed on the right hand side of the dialogue box.

If you move or scroll over to the right side of the `Variable View`

screen, you find three more characteristics not yet discussed: `Columns`

, `Align`

, & `Measure`

.

`Columns`

is optional (it sets the maximal width of the column, e.g. if you need more space to display the variable name). `Align`

can be left as it is.

`Measure`

should be looked at. There are three options: You can specify the level of measurement of the variable as `scale`

(i.e., interval- or ratio-scale numeric data), `ordinal`

, or `nominal`

. Click on each `Measure`

cell, & enter a choice. Insert the appropriate values for each variable in this column. If you don't enter these, `SPSS`

will "guess" which levels of measurement your variables have, and will not necessarily guess correctly. It is useful to record the correct information about your variable, because the `SPSS`

help uses this terminology in explanations, & the graph-drawing procedures expect it.

Before continuing, `Save`

your data file to your drive:

The `Menu Bar`

option `Analyze`

offers a wide range of procedures. We will first look at the `Frequencies`

command, which comes under `Analyze`

| `Descriptive Statistics`

`Frequencies`

is a useful general-purpose procedure, which produces selected descriptive statistics (central tendency, dispersion, skewness, & kurtosis) & charts (e.g., bar charts) for individual variables. You must decide what are the suitable descriptives or charts for the variable chosen. In this section, we illustrate its use for `discrete`

(especially `categorical`

) variables.

Select `Analyze`

| `Descriptive Statistics`

| `Frequencies`

On the left side of the dialogue box is a list of all your variables. In this example, the variables are listed in the same order as in the `Data Editor`

. They may appear differently on different machines (e.g., in alphabetical order). In the list, highlight `sex`

& `ethnicgp`

. To highlight more than one item, click the first, then hold down the `Ctrl`

key while clicking others.

Why are sex and ethnicgp the most suitable variables for this procedure?

Click on the right-pointing arrow , which transfers the highlighted variables to the list on the right. Now click on the `Charts`

button and select `Bar Chart`

. Click `Continue`

, which returns you to the `Frequencies`

dialogue box.

Click the `Statistics`

button which allows you to display various summary statistics. Some of these (e.g., medians, means) only make sense for truly numeric (ordinal or interval) variables, & not for categorical variables such as `sex`

. Others (e.g., quartiles) are applicable to distributions of any variable, but they are of little use with this very small sample. So leave all statistics un-selected, & click `Continue`

. To run the `Frequencies`

procedure, click `OK`

.

The output of the procedure, including the chart, will be written to the `Output1 - SPSS viewer`

window.

The `Output`

(or `Viewer`

) window will simply append output from any process you run. The window is divided into two panes: the *left* pane contains an outline view of the output contents (rather similar to `Windows Explorer`

), while the *right* pane contains any statistical output, charts, or tables you generated during your `SPSS`

session. You can scroll up and down the `Output`

window, expand or move it, or edit it, using the `Edit`

menu functions. Or you can click on the outline headers in the left pane to jump straight to a specified section of output. Pressing `Delete`

will delete the section currently highlighted in the outline.

Look at the output from the `Frequencies`

procedure. One subject has a missing value for variable `ethnicgp`

. How does the output table show that there is a missing-value case?

Can you tell from the bar chart that there is a missing-value case? How can you tell?

Run the `Frequencies`

command again, but this time choose a variable for which it makes sense to calculate a mean or median: A *truly numeric* variable. It can be ordinal or interval-level, but preferably a continuous variable, to provide a clearer contrast with the previous example.

Remove `sex`

& `ethnicgp`

from the right-hand window by highlighting them & clicking the left-pointing arrow. Then move a numeric variable from the left to the right window.

Next, click `Statistics`

& choose an appropriate measure of central tendency for the variable:

Mean or median?

Look at the other possibilities in this dialogue box, & select some others that might be relevant. Click `Continue`

, then `Charts`

. This time, choose a `Histogram`

. It is also useful to select `With normal curve`

. Click `Continue`

, then `OK`

to run the procedure, and compare this output with the preceding one.

How does a histogram differ from a bar chart?

What does the `normal curve`

show?

The previous `Frequencies`

procedure calculated the mean over all subjects. But often you want to obtain the means, or other summary statistics, separately for subgroups of subjects (e.g., males vs. females). Subgroups can be defined by a categorical, or any other discrete, variable. In this case it would be `sex`

. `Compare means`

allows you to do this.

From the `Menu Bar`

, select `Analyze`

| `Compare Means`

| `Means`

.

Make `sex`

the `Independent variable`

(i.e., the one which defines the subgroups). In the `Dependent`

list, put **all suitable** variables (i.e., all the variables for which it makes sense to calculate a mean or median value). One variable is definitely *not* suitable: Which?

Click `Options`

which shows that, by default, the mean, number of cases & standard deviation will be computed for each variable. You can select other statistics by moving them from the left- to the right-hand list. Select the `Median`

(because some of the variables may be ordinal), & any others you want. Click `Continue`

, then `OK`

.

**Note:** if you defined `Value Labels`

for the two values of `sex`

, these labels appear in the output, making it much easier to understand.

Now compute the median value of any one (or more) of the suitable variables separately for each `ethnicgp`

, & inspect the results.

What happens when a person's ethnic group is *not known* (missing)?

**Note:** in real life, & in future classes, you may go on to compute statistical tests of whether, for example, males & females have significantly different means on some numeric variable. **It is highly advisable, before running such tests, to compute descriptive statistics for each of the groups, in the way illustrated here**. By doing this you can ensure that all relevant cases have been included, missing values have been correctly ignored, & you can see how the means & medians differ from each other, which may not always be obvious from the statistical test output.

Finally, choose two variables for which you might wish to display a scatterplot, so that you can view any relationship between them &, if appropriate, test for a correlation.

In this week's class we discussed what **types of variable** are suitable for correlation testing. Correlations test for **monotonic** relationships (where the direction of change between levels of the variables is constant). If there is a relationship but it's non-monotonic, a correlation test may not be appropriate.

Choose a pair of variables for which it is appropriate to draw a scatterplot and, perhaps, test a correlation (*hint: only four of the variables in the dataset are suitable*).

If testing a correlation between these two variables, is there any reason to prefer a parametric or nonparametric test? (Hint: What is the scale of measurement of each of the variables?)

You can use the `Graphs`

option on the menu to produce many types of chart, including scatterplots. For this to work optimally, the `Measure`

column for each of your variables should be defined as `Nominal`

, `Ordinal`

, or `Scale`

.

To draw a simple scatterplot of your two variables, select `Graphs`

| `Chart Builder`

| `ScatterPlot`

(alternatively, use the `Legacy dialogues`

)

The next dialogue box offers many options. The only essential thing is to choose which variables are plotted on the `Y (vertical)`

& `X (horizontal) axes`

, so drag your two variables into the appropriate spaces. There are many other options (e.g., to add a title or vary the legend symbols). Ignore these for the moment. Click `OK`

to display the plot.

Does the plot indicate any *monotonic* relationship between your variables? If so, is the apparent correlation positive or negative?

If you decide to try a different pair of variables before testing the correlation, do so.

To test the correlation, select `Analyze`

| `Correlate`

| `Bivariate`

Insert the two variables for which you drew the scatterplot, into the `Variables`

box. Correlations can be positive or negative, so you can test a directional hypothesis. Thus, you can choose between two-tailed & one-tailed significance tests. **Two-tailed** is more usual; a one-tailed test is only relevant if you have a directional hypothesis, & if the correlation is in the specified direction (next week's class will discuss one-tailed tests). For this example, select **two-tailed**.

You can choose any or all of three tests: Pearson's r (parametric), Kendall's tau-b, Spearman's rho (both nonparametric). For this example, select `Pearson's`

& Spearman's. The dialogue box offers other possibilities, try them if you like. Click `OK`

and look at the output.

There are two output tables, Pearson's (called `Correlations`

) & Spearman's (`Nonparametric Correlations`

). Each cell of the table shows the correlation coefficient, & below it `Sig (2-tailed)`

(i.e., the 2-tailed p-value), & N (sample size after omitting missing values).

Why are some of the entries in the table `1.00`

, with no `Sig`

value?

What is the Pearson correlation between your two variables, its N & p?

What are the equivalent Spearman correlation statistics?

Is the direction of correlation (positive or negative) as expected from the scatterplot?

Is either p-value significant (i.e., p<=.05)?

How closely do the Pearson and Spearman results agree?

If you are familiar with 1-tailed tests, you may know that (*provided the test is appropriate*) it is "easier" to obtain significance with a 1-tailed than a 2-tailed test.

To see how this is apparent in the `SPSS`

output, run the same procedure again, but select `1-tailed`

instead of `2-tailed`

.

The correlation coefficients and Ns should be the same, but the p-values are labelled `Sig (1-tailed)`

& are different from before.

How can you tell that the 1-tailed tests are "closer to significance" than the 2-tailed ones?

You may have noticed that the 1-tailed p-values are approximately half the 2-tailed ones. This is no accident. It should become clear why when we discuss 1-tailed tests in next week's class.

You can use the `Correlate`

procedure to make a table or **matrix** of correlations between all suitable variables, as follows.

Run the procedure again. Insert **all 4 suitable variables** into the box. Select only `Pearson`

& `2-tailed`

.

Inspect the matrix. Note any correlations that are significant at p≤.05, 2-tailed, & note the relevant details (i.e., the variables, the correlation statistic (including the + or -), N & the p value.

Because `SPSS`

shows exact p-values, we can see which effects "just miss significance" (i.e., have a p-value which is only just greater than .05), & which effects are a long way from significance (much larger p-values). As we will discuss in next week's class, it is often useful to know which effects are **marginally significant** (i.e., have a p-value which is >.05 but ≤.10).

One correlation is marginally significant. Which?

All other correlations in the table have ps>.10 (i.e., they are a long way from 2-tailed significance).

You can save the whole output as a file for later use, & you can print them.

Output files are quite large, especially if they contain charts, so, before saving or printing, check through to decide whether you need it all. To remove a section, click on the section of output, or on its label in the left-pane `Outline`

. Then either press `Delete`

, or use `Edit`

| `Cut`

, which allows you to restore it (with `Edit`

| `Paste`

) if you change your mind.

**Save your output to disk**. `SPSS`

automatically gives the filename an extension of `.spo`

or `.spv`

, identifying it as an `SPSS`

`Output Viewer`

file. In `Windows Explorer`

, you may not see the extensions `.sav`

& `.spo`

, but they will have different icons, & may be labelled `SPSS Data Document`

& `SPSS Viewer Document`

respectively.

**Note:** The output file is in a format specific to `SPSS`

, & the file can only be read into `SPSS`

(& only to the version of `SPSS`

that created the output. It cannot read directly into a word-processing package. To read a previously saved output back into the `Output Viewer`

window of `SPSS`

, select `File`

| `Open`

| `Output`

.

You can copy & paste sections of output, including charts, from `SPSS`

into word-processing documents. There's more on this later in the course.

If you restart `SPSS`

, the opening dialogue gives you the choice of typing in new data, or opening an existing data file (`Open an existing data source`

, plus a list of recently used data files. Find your file...).

**To read in a data file** select `File`

| `Open`

| `Data`

. Click at the right-hand end of the `Look in`

box, switch to the correct location, find your file, click on the filename, then click `OK`

.

You can also open saved output files into the `Output Viewer`

window: Select `File`

| `Open`

| `Output`

. `SPSS`

can have several output windows open at a time. If you already have an `Output Viewer`

active, & you open another output file, the latter will be opened into a second output window with a different name.

You probably noticed that most `SPSS`

dialogue boxes have a `Help`

button. This provides *context-sensitive help*. This is the simplest way to get help, but you should also become familiar with it more generally.

With `SPSS`

running, click on `Help`

in the main menu bar. The pull-down menu contains various options (e.g., `Statistics Coach`

, which will guide you through the correct statistics to use for your data type(s), & `Tutorial on SPSS`

which you might find useful some time.

For now, select `Topics`

. This contains a `Contents`

option (contents), `Index`

(alphabetical list), & `Search`

(search for specific words / phrases).

Select `Contents`

. This opens a `Contents`

panel, with a list of items. You can double-click on any of these to display further information about that topic. The `Back`

& `Forward`

arrows in the menu bar move you through pages. Spend a little time familiarising yourself with the help system. When you have finished, close the help window.

There is a simple online help site created by the University of Birmingham (click on *The How To Guides*, choose SPSS 10.0 (an early version, but the basic procedures are similar to later versions). From there on it should be easy. Try the *Test Spotting Quizzes* too.

There are very many books on `SPSS`

. Most of them would not be useful for you, many go into great detail about how to use `SPSS`

, but give little guidance on choosing statistical methods, or else give guidance which is incomplete or misleading, or else only cover elementary methods. The following are recommended, however.

`SPSS`

informormation on the book's website
`SPSS`

for beginners. There are many examples and instructions in later chapters.
`SPSS`

to important statistical concepts. It includes a wide range of `SPSS`

procedures, and some advanced statistical methods; but the coverage of the more advanced methods tends to be incomplete.