There are 4 main windows in SPSS and we thought it would be useful to introduce you to what the 4 windows in SPSS are and what each is used for:

1. Data View - The data view is used to store and show your data. It is much like an ordinary spreadsheet although in general the data is structured so that rows are cases and the columns are for the different variables that relate to each case.

2. Variable View - The variable view contains the variables on your data set, so it defines the properties of your dataset. Each row will define all of the various variables for one set of data. For example, for a numerical piece of data this would show (amongst other things) the number of decimal places that are stored for that piece of data.

The variables include - name, type, width, decimals, label, values, missing, columns, align and measure. Ensuring that the 'measure' of your variables is correct is vital. The variable can be Nominal which is for strings of data, Ordinal for data that isn't continuous but can be ranked or ordered or, finally, scale which is used for a variable that is continuous, for example a distance to somewhere.

3. Output Viewer Window - This window is used to show the results that have been output from your data analysis. Depending on the analysis that you are carrying out this may include the Chart Editor Window or Pivot Table Window.

4. Syntax Editor Window - This window shows the underlying commands that have executed your data analysis. If you are a confident coder this is where you can amend the code, or write your own from scratch, and then run your own custom analysis on your data set.

So there you have it, a whistle stop tour of the 4 main windows that you will find in SPSS.

Another simple trick we like to teach during our SPSS training courses is how to use the find and replace function. This is a basic option within the program that you will find in other types of commonly used software such as internet browsers, word processors and spreadsheets. However, like many quick shortcuts, we find that some users are unaware of its existence.

Find and replace does exactly what it says on the tin. It allows you to rapidly find information within your data and replace it with whatever you want. So let’s say you have a mistype in your data. One of your respondents is called Alastair, but during data entry you have accidentally spelled it Alasdair. If you have masses of data in your data view it might be difficult to find his entry, but if you hold down the ‘control’ (or ‘ctrl’) button on your keyboard and then click the ‘f’ key, a box will pop up asking you what you would like to find. In the ‘find’ field, write ‘Alasdair’, in the ‘replace’ field, write ‘Alastair’.

Now, your next move depends on the contents of your data. In the ‘match’ section, you will be asked where you would like to look for the information. If your first names are in one column by themselves, you can select ‘entire cell’, which will replace the information in an entire cell. But if your first and surnames are entered into the field, you will need to click ‘begins with’ to replace only the first name.

If you know you have only one Alasdair within your data, click ‘replace all’. This will find and replace all instances of Alasdair. An information box will appear to let you know how many instances were found and replaced. If you are not sure how many Alasdairs you have, click ‘find next’ and keep going through the instances until you find the one you are looking for. Then click ‘replace’. This will ensure not all Alasdairs within your data have their names respelled.

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When learning SPSS, it is easy to miss some of the time-saving shortcuts that could make your life easier. One trick is using the copy and paste function in the program to save you having to write out identical information multiple times.

Let’s say you have conducted a survey where respondents were asked to choose a number between 1 and 5 to illustrate how much they are attracted to a series of pictures shown to them. The first thing you should have done with this information in SPSS is to create a numerical variable with the Values tab set to show 1=strongly dislike, 2=dislike, 3=quite like etc.

If you have shown your survey participants 20 pictures, you may think you need to list 20 variables in the variable view and write out these identical values for each one, but there is a quicker way around this.

First write out your first variable in the row number 1 of the Variable View. Then right click in the left-hand bar on ‘1’ and select ‘copy’. Next, left click on number 2 in the left-hand bar and hold and drag all the way down to ’20’. Now, right click anywhere within the selected rows and select paste.

You will now have 20 variables that each have the same values. The new variables will have default names such as VAR0002, so you will need to go through these and name them in a more meaningful way, as well as changing any other information in the variable fields to match your survey.

This is a great way to make data entry on surveys with a large number of similar questions quick and easy. Stay tuned for more tips from our SPSS training courses!

Click here for more details of Acuity Training's introduction to SPSS training courses.

This is one of the simplest yet most effective tips that we give delegates when they attend our SPSS training courses.

If you have a large amount of data, you may consider selecting just one section of it in SPSS to make your analysis easier. One way to do this is to learn to use SPSS’ Select Cases function to choose data associated with a time period - such as between a selection of dates. This is helpful if the raw information you have includes dates of birth, for example. The dates must already be entered into the Data View spreadsheet under a Variable - which we will call Date. They should also be entered in the format day, month, year.

So now you are ready to perform the Select Cases function. First click onto Data in the main toolbar, then Select Cases. On the first box of options that appears, click If Conditions Satisfied, then click OK. On the second box that opens you will need to perform something a little more complex. You will need to type out your own syntax. But once you break it down into steps, this is easier than it sounds.

First of all, click Search in the Function Group dropdown list and Range in the Functions and Special Variables list. The code RANGE(?,?,?) should now appear. It is your job to replace the question marks with meaningful instructions for SPSS to follow.

In place of the first question mark, let SPSS know which variable you want it to focus on. So in this case, you would write ‘date’. Now go on to the second question mark, making sure you leave the comma between the question marks in place.

Here SPSS needs to know the range of dates you want to select and the format they are written in. Write ‘date.dmy’ (the ‘dmy’ stands for day, month year) and then indicate the lowest end of the range you are interested in using the day, month, year format and close it off in brackets. So let’s say you’re looking at data starting on the 1st January 2012, you would write (1.1.2012).

Now you’re going to do exactly the same for your highest end of the range, which we’ll say is 1st August 2012. So in place of the third question mark write ‘date.dmy(1.8.2012)’.

So your code should look like this: RANGE(date, date.dmy(1.1.2012), date.dmy(1.8.2012))

Before you submit the code, make sure you have a double parentheses at the end of it to close off both sets of brackets, then click Continue and then Ok. Now SPSS will block of data outside of those dates.

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So you have completed a survey and you want to be able to assess how probable it is that a respondent will say yes or no to one of your variables. With a little bit of SPSS training, this is a very simple thing to do. 

For example, you have conducted research on whether females and males, with and without jobs, are likely to be pet owners. The fact that these variables are dichotomous (each is simply a yes or no answer) makes it a little easier to check the odds of who is most likely to be a pet owner while using SPSS. When your variables have a wider range of possible responses, it gets a little more complex, but that’s something for another blog post. Let’s just start simply for now.

First, enter your data. Create three variables on the variable view tab called Pet Owner, Employed and Gender and fill in your responses on the Data View tab. Remember, you do not need to put ‘yes’ and ‘no’ responses into the Data View spreadsheet. By using the Values tab on the Variables View you can team numerical data with descriptions, so you could team ‘male’ with 1 and ‘female’ with 0, for example.

Now you need to perform a binary logistic regression analysis. Click on ‘Analyse’ in the toolbar and then ‘Regression’ and then ‘Binary Logistic’. Put ‘Pet Owners’ into the dependent variable space and then Employed and Gender into the Covariables space, then click ‘Ok’.

You will now be presented with a list of tables. The most important ones you need to look at are the Classification Table in Block 0, which will tell you how many people, unemployed and either female or male, are actually pet owners. Then look at the Classification Table in Block 1, which will show you what numbers the data predicted. If you have a good model to make predictions with, the numbers in Block 1 will be similar to or exactly those in Block 0, but if the predictions are way out, you may need to reconsider what data you use and how you deploy it.

Click here for further details of Acuity Training's SPSS training courses.

So here is a question that gets asked quite often in our SPSS training courses - what exactly is the null hypothesis and how does it apply to may statistics.

Each time that we want to run a statistical test we need to formulate two hypotheses, and we then use the statistical test in SPSS to test between those two hypotheses. The null is the simplest explanation or hypothesis and the one that the researcher is trying to disprove . The alternative is usually the hypothesis that we want to test either for to prove or disprove depending on the circumstances. 

Choosing an example to illustrate this. If we took a sample of 10 people at random from a larger population that we assumed that had equal numbers of men and women in it and found that 6 were female and 4 male, then the null hypothesis would be that there are equal numbers of men and women, whereas the alternative would be that there are more women than men in the population.

Having formulated our hypotheses we then run a test to test to test the hypotheses. Given that SPSS will know the distribution for the results of the test we can then calculate a p-value (significance) value for this data. This tells us how unlikely our result would be  if the null hypothesis is true. If the p-value is small then we reject our null hypothesis.

The subject of p-values and their interpretation will be the subject of a further blog post later in the week. So check in later in the week to see the next in our series of questions that come up frequently during our SPSS training courses.

Click here for more details of Acuity Training's introductory SPSS training courses.

So you have completed a survey and you want to be able to assess how probable it is that a respondent will say yes or no to one of your variables. With a little bit of SPSS training, this is not a hard function to perform.

Let’s say, as an example, you have conducted research on whether females and males, with and without jobs, are likely to be pet owners. The fact that these variables are dichotomous (each is simply a yes or no answer) makes it a little easier to check the odds of who is most likely to be a pet owner while using SPSS. When your variables have a wider range of possible responses, it gets a little more complex, but that’s something for another blog post. Let’s just start simply for now.

First, enter your data. Create three variables on the variable view tab called Pet Owner, Employed and Gender and fill in your responses on the Data View tab. Remember, you do not need to put ‘yes’ and ‘no’ responses into the Data View spreadsheet. By using the Values tab on the Variables View you can team numerical data with descriptions, so you could team ‘male’ with 1 and ‘female’ with 0, for example.

Now you need to perform a binary logistic regression analysis. Click on ‘Analyse’ in the toolbar and then ‘Regression’ and then ‘Binary Logistic’. Put ‘Pet Owners’ into the dependent variable space and then Employed and Gender into the Covariables space, then click ‘Ok’.

You will now be presented with a list of tables. The most important ones you need to look at are the Classification Table in Block 0, which will tell you how many people, unemployed and either female or male, are actually pet owners. Then look at the Classification Table in Block 1, which will show you what numbers the data predicted. If you have a good model to make predictions with, the numbers in Block 1 will be similar to or exactly those in Block 0, but if the predictions are way out, you may need to reconsider what data you use and how you deploy it.

One of the first things people notice during our SPSS training courses is the similarity between SPSS and spreadsheets like Excel. Indeed, the data entry page you are presented with is much like a basic spreadsheet, but there are a few differences that need to be grasped before you can get to grips with the software.

In SPSS the horizontal rows are dedicated to each item of data (for instance each respondent to a survey), while the vertical columns are to be used for each variable (such as their height, weight, or IQ - whatever data you have collected).

While this might seem simple enough, it is important you do not just jump in transferring data to the spreadsheet. First, you need to learn a little about preparing the vertical columns.

If you do not name your columns, SPSS will choose a default name like VAR00001. To change the names to ones that are more meaningful to you, click on the ‘Variable View’ tab in the bottom left of the screen. SPSS will not allow spaces or punctuation to be used in the name column here, so if you need to separate words, use an underscore.

While you’re there, make sure your columns are prepared to format your data properly. Click on type and select between string and numeric. String should be used if you are going to be entering letters or words and you do not want SPSS to recognise the data as a number.

You might also need to change the width too - that’s the number of characters SPSS will allow you to enter for each entrant in that variable. Decimals refers to how many decimal points you would like to include after your numerical data, the Label is a space to briefly describe your variable (with spaces allowed this time!), Values allows you to team up numerical and descriptive terms (for instance where 1=male and 2=female) and Measure allows you to tell the programme whether your data is nominal, ordinal or scale.

The Column and Align fields are more to do with the presentation of the data, to make it easy to read. Just like in an Excel spreadsheet, you can decide how wide the columns should be and whether the text should be aligned to the left, centre or right.

Make sure you have prepared your columns in the Variable View before you start entering statistics in the Data View, otherwise you could soon find yourself swimming in unlabeled data!

There are more tips from our SPSS training courses coming soon.

Looking for easier, more efficient ways to use SPSS? As a complex and comprehensive program, even those who are used to the software may be missing out on some of the tips and tricks that can make analysing data with SPSS a piece of cake. In fact, it’s often some of the more basic ideas that catch people out, even after they have been on an SPSS training course. Check out our top five SPSS tips below for some quick and practical hints.

1) Save your master copy

It’s a really good idea to back up your original data so you have a master copy to return to should you wish to take your analysis down a different path. Leaving a paper trail as you go along, by saving as a new file each time you make a significant or permanent change to your dataset, is another top tip, as this allows you to revert back to different data transformations and analyses as and when you need to.

2) Start simple

Don’t jump in at the deep end with complicated mathematical analyses without finding out what simpler information you can obtain from your data set. Start with a descriptive analysis to provide a context to more complex procedures, and use the information you glean from this to guide you when investigating further patterns.

3) Keep your software up to date

To get the most out of SPSS, make sure you are keeping your software up to date. This does not necessarily mean buying the latest version of SPSS. The manufacturer releases updates and patches on the program frequently and downloading these updates ensures your SPSS software remains effective. To download patches, click on Help, then Check for Updates.

4) Take advantage of the temporary command

The temporary command does exactly what is says on the tin - it performs temporary data transformation which is then reversed after the first procedure you run.

This can be a valuable tool, particularly if you are analysing large datasets that could take quite a bit of time to run a syntax on. Instead, it is possible to perform a transformation on a subset of the data as a test, before running the same analysis on all of your statistics.

5)  Keep an eye on your output files

SPSS allows you to have several output files open at once, but it will only transfer data to the one that is “active”. Make sure you are aware which file is active so you can keep an eye on where your analyses are ending up.

Most people find it daunting to start learning a new computer program, especially when it is niche software they may never have encountered before. However, once you have learned a few basic rules about the program and practised using it with the guidance of an expert, almost all software can become second nature eventually - after all, developers pay a lot of attention to making their products as intuitive as possible.

When it comes to opening SPSS for the first time, it can look like there is simply too much to learn, which is why hiring an SPSS training is most people’s first port of call. This is a great investment for the future, as nothing really beats having an SPSS expert take you over the basics and answer your questions face to face. Even once people have been using SPSS for a while, they often attend SPSS training courses to brush up on their skills and learn more advanced uses of the software, particularly when upgrading to a new version of the program.

But even before you hire a tutor, there are some things you can do by yourself to make the learning process that little bit easier. Take some time to sit down and look over the program, instead of trying to jump straight in with a data set and feeling your way through it.

A good tip is to familiarise yourself with the layout of the tools SPSS provides by clicking through the toolbar menu. When it comes to learning how to use the program it will be invaluable to have some sense of where each function is located and what tools are available to you.

The ‘Help’ section of SPSS is one extremely useful tool to help you learn more about the program - with a full manual available within the software itself - and this is a good place to start as a beginner. To access instructions on how to use each function, simply click on the function you wish to use and in the box that appears, click ‘Help’.

While this is a very basic way to start with a new program, taking small steps like this can really help you retain knowledge in the long run, so try not to rush ahead.

Stay tuned for more in depth tips on using SPSS.