Detail from Tim Macer's ASC 2010 presentation
While detractors have been denouncing PowerPoint as a vehicle for presenting research results for several years now, MR seems to be as wedded to it now as at any time in the past. It’s a topic Tim Macer, MD of meaning ltd explored at the ASC conference in London “Putting the Pizzazz into Research: renewing the rules of engagement“, in the event’s closing talk “It doesn’t have to be PowerPoint”. In the presentation, Tim observed that PowerPoint is a provided as a client deliverable on 54% of research projects, and examined some of the problems that critics have identified in the use of PowerPoint. He then offered six “antidotes to PowerPoint.
Tim introduced these by explaining: “These are my subjective pick of software products I have seen over the past year where I’ve seen providers offering something that is useful and different in this area. ” Tim’s presentation was given with the aid of one of the featured tools – Prezi – a new and quite ground-breaking presentation tool.
What it does
Report automation platform which takes tabular output from almost any MR cross-tab program and transforms it into well-crafted PowerPoint presentations. Works with existing slide decks or will generate new ones selectively, directly from tables.
Ease of use
Compatibility with other software
Value for money
Annual subscriptions from £5000 for a single user, £5,850 for 2 named users, £10,500 for 10. Concurrent pricing options also available. Prices include training, support and updates
- Saves hours when converting tables into presentations
- Greatly reduces the scope for errors when creating presentations
- Shared templates reduce work and allow for a custom look for each client
- Presentations can be updated with new data even after they have been modified in PowerPoint
- No full preview without exporting to PowerPoint
- No undo when editing or making changes
- Windows only
It’s a program that few profess to love, but PowerPoint shows little signs of yielding its iron fist over the boardroom presentation yet. Researchers often feel they are slaves to the god PowerPoint twice over, not just when presenting, but when preparing too due to the sheer monotonous drudgery of creating each slide by hand – slowly patting figures and graphs into shape, often against the pressure of the clock.
Rosetta Studio automates this process, and does it in a very efficient and comprehensive way. As a tool, it’s been around for over five years now. We reviewed version 1 back in 2005, when it was simple and straightforward to use, but fell short of doing all you needed it to. There wasn’t enough control over style and layout to create client-ready presentations, and this inevitably resulted in having to do a lot of finessing on the final output within PowerPoint to get the look right, and leaving you vulnerable to having to repeat all this work if you needed to re-run the data.
Improvements since then, culminating in version 3.3, have removed all these limitations. The range of capabilities has, quite simply, exploded. Pretty much any tabular input is now supported, either through built-in importers, or by using an upstream mapping utility. Within the tool, there is now fine control over every aspect of output, and a lot of attention has gone into providing ways to prevent anyone from every having to do anything more than once.
As an example, colouring, shading and chart options are all created and controlled within Rosetta and are not limited to what Excel or PowerPoint can produce. Colours can be linked with products or even applied automatically from the labels in your data for brand names, company names, countries and so on. It eliminates any fight you may have with PowerPoint showing different colours from the ones you had hoped for because of the clumsy way that PowerPoint templates and document themes interact. Instead. All of this is controlled, safely and predictably from within Rosetta, yet still it is standard PowerPoint that comes out of it.
A very powerful feature of the tool is the template. These takes a little time to master, but templates have the advantage that, once defined, they can be used across the whole organisation and shared easily among teams. Using templates, it takes just seconds to build charts from tables. Templates not only apply the styling, but work out what to pick out of the raw table – e.g. just the percentages or the frequencies, and not the total columns and rows.
Not everyone needs to become a template guru. It is not hard to modify them, or adapt them – but if you want to ensure a consistent look, and to control this, they can also be password protected against unwanted or unintentional changes.
There are now three modes of operation: generate, populate and update. In version 1 only generate was possible – this limited Rosetta to the “push” method of production, where you effectively created then exported a PowerPoint from scratch. Generate is ideal for ad hoc work, but not much help for any kind of continuous or large scale production.
Populate mode introduces the alternative “pull” method, where you can take an existing PowerPoint and link it to tables within Rosetta Studio by going through the PowerPoint document, stripping out the variable content and replacing it with a tag that will pull in the relevant data from Rosetta. Tags can pull rows, columns, individual cell, tables or sections of tables, and are to some extent smart – e.g. you can pull the first row, the second from last cell, or the column headed ‘females’. ATP’s implementation is delightfully simple, though it does take some effort to get your mind around the process. But it is ideal for large-scale report production on trackers, where many similar reports are produced on different cuts of the data, and the suite provides some batching and automation modules for power-users that go beyond the desktop interface.
Even more ingenious is the new “update mode” which achieves a kind of magic with PowerPoint. There is nothing to stop you from going in and making extensive changes to your presentation and still be able to update the PowerPoint, for example, because you had to remove a case and re-weight the data. Rosetta invisibly watermarks each chart it produces and uses these hidden tags to identify each value correctly and substitute the updated value. It’s very clever.
All this increased sophistication does come at a cost, however, as the price has nudged upwards, and so too is the investment you need to make in learning it. This is not something you can pick up for the first time and guess your way through. ATP Canada encourages new all users to take their two-day training course before getting started, by including it ‘free’ within the licence fee. The program is reasonably intuitive, once you have got to grips with the fundamentals, though it’s a pity that you cannot get a better preview of what you are doing within the Rosetta interface – you only see it properly when you generate the PowerPoint. If you find yourself going along the wrong track, it does not provide any real undo capability either, which is a pity.
Producing presentations is complex and without something like this, is very time-consuming. Speaking to actual users, it’s clear that users not only find learning it an investment work making, but that they soon wonder how they ever managed without it.
Customer viewpoint: Leger Marketing, Canada
Leger Marketing is a full service research company with nine offices across Canada and the United States. Here Christian Bourque, VP Research and Patrick Ryan, a Research Analyst at Leger Marketing, speak of their experiences with Rosetta Studio.
CB: “At the time we were looking for something, we felt most automation software was aimed at large trackers. About eighty per cent of our work is ad hoc. We needed something where the up-front time would be quite small. Rosetta Studio seemed to be better-designed for ad-hoc research, certainly at that time. “
PR: “Now, nearly every one of our quantitative projects runs through Rosetta at some stage. We’d even use it for a four question omnibus study, where it is still faster than creating a report slide-by-slide. It means the bulk of our time is no longer taken up with the work of creating a report. The focus is now on analysing and interpreting the data.”
CB: “Once you have spent a little bit of time devising your own templates, you will save 60 to 75 per cent of your time analysing the data. “
PR: “Something that would take four days or a week to put together is now taking us one or two days. “
CB: “It not only saves the analyst time, but you also need to consider the quality review perspective. We used to do a line-by-line review. Now, because it is automated, this is no longer necessary. It’s a load off our shoulders. It means we can spend more time improving the quality of the insights. We also find we can include more complex cuts of data in the report that we would not have had time to do, beforehand, like that little bit of extra multivariate analysis.”
PR: “Something we like a lot is the flexibility it gives you to try different things. You might be creating a set of graphs and you realise it could be better presented another way. Now the hassle of changing your graphs or charts isn’t such a big deal. It takes you two seconds.
“It takes two days to learn, though the basics can be covered in a morning. It is fairly intuitive. We have a couple of reports where the analysis use the tagging. The interface is the same but the logic is different. You have get your mind around how to use and place tags, but once you have done one it is fine. It’s actually very simple.”
CB “We like the flexibility it provides from a look and feel point of view. We can have different templates for different companies. Many of our client have a corporate library of anything they generate, so when it circulates on the client side, it needs to look as if they it’s their document.
“This is something we introduced to add value, not to reduce staffing. It’s the nature of our business that you constantly have to be faster than the year before. The demand on time is extreme. This is one of the ways we’ve been able to meet that challenge, while improving quality. And the other major demand is for better insights, and this is one of the tools that allows us to do that.“
A version of this review first appeared in Research, the magazine of the Market Research Society, February 2010, Issue 524
What it does
A collection of graphical data delivery tools, based around a single interactive and highly responsive for integrating streams of research and non-research data. May be used as an alternative to dashboard reporting.
Ease of use
Compatibility with other software
Value for money
Per project. Viewers, from £1000 for a one-off project, £2000 annually for trackers. Unlimited users, includes Espri data analysis tool. Consoles: from £20,000.
- Integrates diverse data streams into a single interactive view
- Compatible with any web browser
- Compelling style of presentation makes complex data highly accessible
- Architecture an interface places limits on the number of variables or dimensions that can be included
- No tool to build databases: only in conjunction with Infotools’ data import services
- Expensive for small-scale uses.
Simplicity is the hallmark of good design, whether it is for a mobile phone, a website or a piece of software. It is something we are surprisingly haphazard at achieving in market research. Compared to other kinds of corporate data, survey data tends to be complex, and the analysis and reports that come out of the other end all too often reinforce rather than reduce complexity by stripping it back to the essential. For New-Zealand based MR software provider Infotools, the aim of a new range of data reporting tools, which they call simply Viewers, is to pare back on clutter, and bring radical simplicity to data analysis.
In this initial release, there are four different Viewers to choose from, InfoPlot, InfoSwitch, InfoTrend and InfoWorld. Each follows the same principles and uses a single chart, accompanied by a small amount of tabular data, to present a related series of measures. These would typically be brands or product categories. Each Viewer is aimed at presenting particular kinds of data. InfoSwitch, for example, presents brand switching behaviour. They are all minimalist Web 2.0 products – users are hardly aware they are using software. Instead, they interact with a single webpage containing a chart, sometimes a few figures too and some lists of hyperlinks to other items.
The clean design approach means there are no legends with the charts. Each bar, line or pie slice always has its own label, and the software determines that the labels are always visible, even on quite crowded charts. For instance, in the InfoTrend viewer, mouse over a trend line, and it will highlight it, and the others will shade out. Click on the trend line, and it will split it at that point, and redraw the chart to show trends before and after that point.
All across the range of tools, the charts respond intuitively to pointing, clicking and dragging, in ways that avoid the need for any kinds of explicit controls. There are no lists of properties, no menu of options, no clutter: all the control is ceded to the actual data being viewed. Other items are listed on screen to allow you to vary the view. These are organised into are measures and demographics. Measures could be from the same survey, e.g. brand awareness, affinity or association, but very powerfully, they could be from other sources: marketing spend, retail performance and so on, all scaled to the same axis. You just pick the ones you want to see together.
Other viewers offer different views. InfoWorld presents data plotted over a world map, and maps covering other geographic regions are also feasible. InfoPlot brings to live two-dimensional plots or perceptual maps, with the same highly intuitive approach to encourage experimentation.
Brand managers could get very interested in InfoSwitch – if data are available on brand loyalty, previous brands or alternative brands used. This shows each brand as a sphere orbiting other brands, and the strength of the links between them, with all the Viewer possibilities of moving items around, filtering them and focusing on subgroups, to really understand the interplay of brands, as consumers divide or shift their loyalties.
The Viewers offer great flexibility in deployment. You could build them into an intranet, publish them on a website or you could email links to recipients for them to download a package they can run locally on their laptop without for a permanent internet connection. The technology is built on Microsoft’s excellent Silverlight technology ¬– something we are going to be seeing much more of, in software applications for MR. It will run on any browser and any platform.
Infotools also offer what they call Consoles, which are bespoke data delivery portals based on one or more customised Viewers – they are deliberately avoiding calling these dashboards, though the result is definitely dashboard-like in its approach.
Is there a catch? Well, there are potentially two. For many, a deterrent is that both Viewers and Consoles are not offered as standalone software – they are tied into Infotools’ service offer. Only Infotools can build the underlying database and populate your Viewer with your data. However, the database is in the same structure as for its full-blown Espri cross-tab and analysis tool. They will throw in a copy of this for free if you are using a Viewer. This has the advantage that the numbers in published data and any subsequent ad hoc requests are likely to agree, as they come from the same data source.
The other catch is the constraint imposed by the interface on the number of variables or dimensions you can include. There is space for around 70 items, but as this includes all measures and all the categories of any demographics, you could struggle to accommodate all you need from a sizeable tracker. However, even here, this discipline could be an advantage, as it forces you to focus on the essential. After all, some do say that less is more.
Customer viewpoint: Othman El Ouazzani, The Coca-Cola Export Corp., Casablanca, Morocco
The Coca-Cola Export Corporation, North & West Africa Business Unit has implemented an Infotools Console based around the new Viewer technology. Knowledge & Insights Manager Othman El Ouazzani, based in Casablanca, Morocco, worked closely with Infotools during the implementation, and introduced the new console to around 25 regional marketing managers and national sales managers.
The console brings together data from a wide range of sources, merging sales data, retail audit data and consumer tracking data from different fieldwork suppliers, media data and even data on weather and temperature.
“All these streams of data can be very difficult to look at or grasp in an in,” Othman explains. “So the idea was how to integrate all of this data into one platform – so as to make it easy for the user to see not all of it, but the most important parts of it so they can make sense of what is happening with the business and the with the brands.”
Infotools took the team through a process of identifying the data and what the users needed to see. “We had to limit ourselves to the most important things, which was difficult, but it was also a valuable activity. This process took us about 3 months, from inception to the first run of the Console.”
The Console was designed to meet the needs of different users within the company – providing high-level views for the marketing unit, as well as individual country views for the different national managers.
“The success we have had lies in the fact that we are able to integrate all of this information within one interface. We can chart sales data with retail data, brand equity data plus media data. You can see all of them, all at once, on the same chart. This makes it very easy to navigate your way through all of this information and intuitively drill down if you wished so.
“It does not require any training – it is so self-evident. If you are an Internet user, it is exactly like using the Internet, except that it does not take you to a new page each time, it just displays everything on the same page”.
Othman advises anyone considering this approach to “make it simple for the users from day one – don’t try to complicate it. You have to be very picky on what dimensions to include in each matrix. Recognise you cannot satisfy everyone’s needs with one tool. The user eventually finds that all most important information is there, and they can refer to the databases directly if they need more.
He concludes: “One of our biggest constraints is time. This allows us to have a very quick view of the current situation – how the brands are doing, the volumes and so on. It is quite easy to use and it saves you quite a lot of time. Before, it would have been hours and hours of work to do this, but now it is available in just the click of the mouse. “
A version of this review first appeared in Research, the magazine of the Market Research Society, January 2010, Issue 523
What it does
Modern, GUI-driven cross-tabulation, analysis and charting suite for market research data aimed at the tabulation specialist. Capable of handling large and complex data sets, trackers and other ‘difficult’ kinds of research project.
Red Centre Software, Australia
Ease of use
Compatibility with other software
Value for money
Full version $4,800 (allows set-up); additional analyst versions $2,400. Annual costs; volume discounts available.
- Cross-tabs and charts of every kind from large or complex datasets, and so much more
- Quick and efficient to use for DP specialist, using a choice of GUI access and scripting
- Push-pull integration with Excel and PowerPoint for report preparation and automation
- Superb proprietary charting to visualize MR data more effectively than in Excel or PowerPoint
- Excellent support for managing trackers
- Interface is bewildering to beginners: a steep learning curve
- No simple web-browser interface for end users or to provide clients with portal access to studies
We always try to present something new in these software reviews, but this time, we think we are onto something that could break the mold: a new tabulation software package from an Australian producer, Red Centre Software, that leaves most of the existing choices looking decidedly dated. It’s refreshing, because for a while, most efforts in market research software seem to have gone into improving data collection and making it work across an ever-broadening spectrum of research channels. Innovation at the back-end seems to have focused on presentation, and has often left research companies and data processing operations with a mish-mash of technology and a few lash-ups along the way to transform survey data into the range of deliverables that research clients expect today.
Ruby could easily be mistaken for yet another end-user tabulation tool like Confirmit’s Pulsar Web or SPSS’s Desktop Reporter, with its GUI interface and drag-and-drop menus. The reality is that it is a fully-fledged tabulation and reporting system aimed squarely at the data processing professional. If you are looking for a Quantum replacement, this program deserves a test-drive.
As far as I could see, there were no limits on the data you could use. It will import data from most MR data formats, including Quantum, Triple S and SPSS. Internally, it works with flat ASCII files, but it is blisteringly fast, even when handling massive files. It will handle hierarchical data of any complexity, and offers the tools to analyse multi-level data throughout, which is something modern analysis tools often ignore.
It is equally at home dealing with textual data. The producers provided me with a series of charts and tables they had produced from analyzing Emily Brontë’s Wuthering Heights by treating the text as a data file. The same could be done for blogs, RSS feeds and the mass of other Web 2.0 content that many researchers feel is still beyond their grasp.
More conventionally, Ruby contains a broad range of tools specifically for handing trackers, so that you are not left having to automate the reconciliation of differences between waves due to variations in the question set and answer lists.
Ruby is a very intelligent tool to use when it comes to processing the data. The data in the tables reported or charted in MR have often gone through a long chain of transformations, and in the old tools, there could be yards of ‘spaghetti code’ supporting these transformations. Trying to work out why a particular row on a table is showing zeroes when it shouldn’t do can take an age in the old tools, as you trace back through this tangle of code, but Ruby will help you track back through the chain of definitions in seconds, and even let you see the values as you go. It is the kind of diagnostic tool that DP professionals deserve but rarely get.
In Ruby, you will probably make most of these data combinations and transformations visually, though it does also allow you to write your own syntax, or export the syntax, fiddle with it, and import it again (the combination that DP experts often find gives them the best of both worlds). However, Ruby keeps track of the provenance of every variable, and at any point, you can click on a variable and see exactly where the data came from, and even see the values at each stage.
The range of options for tabulation and data processing is immense, with a broad range of expressions that can be used to manipulate your data or columns and rows in tables. There is complete flexibility over percentaging and indexing values off other values, or basing one table on another, so it is great for producing all of those really difficult tables where every line seems to have a different definition
With charting, Ruby gives you the choice of using its own proprietary charting engine, or pushing the data out to PowerPoint or Excel charts. The native Ruby charts are a treat to work with, as the developers seem to have gone out of their way to redress the inadequacies of Excel and PowerPoint charts. For time-series charts, concepts such as smoothing and rolling periods are built-in. You can add trend lines and arbitrary annotations very easily. Charts can be astonishingly complex and can contain thousands of data points or periods, if you have the data. Yet it will always present the data clearly and without labels or points clashing, as so often happens in Excel.
Excel and PowerPoint charts are also dynamic, and the Ruby data source will be embedded in the chart, so that the charts can be refreshed and updated, if the underlying data changes.
Amy Lee is DP Manager at Inside Story, a market research and business insights consultancy based in Sydney, Australia, where she has been using Ruby for two years, alongside five other researchers and analysts. Ruby is used to analyze custom quantitative projects and a number of large-scale trackers.
Asked if the program really did allow a DP analyst to do everything they needed to, Amy responds: “We were able to move to Ruby a couple of years ago, and it is now the main program we use, because it can do everything we need to do. I find it is an extremely powerful and flexible tool. Whenever I need to do anything, I always feel I can do it with Ruby. Other tools can be quite restrictive, but Ruby is very powerful and completely flexible.”
Amy considered the program went beyond what more traditional DP cross-tab tools allowed her. She observes: “Compared with other programs I have used, Ruby allows me to filter and drill down into the data much more than I could with them. It’s especially good at exporting live charts and tables into documents.
“Once they are in PowerPoint or Word, trend charts can be opened up and adjusted as necessary. When it is a live chart, it means you can update the data, and instead of having to go back to Ruby, open it up and try, find the chart and then read the data, you can just double click it inside PowerPoint, and you can see all the figures change. And there is even an undo feature, which is good for any unintentional errors.”
Amy freely admits that this is not a program you can feel your way into using, without having some training, and allowing some time to get to understand it. “It is really designed for a technical DP person,” she explains. “If you have someone with several years’ experience of another program they will have no problem picking this up as everything will be very familiar to them. But we also had a client who wanted to use it, someone with a research rather than a DP background, and they found it a bit overwhelming, because it can do so much, and it is not that simple. It looks complex, but once you get the hang of it, you can do what you need very quickly.”
Among the other distinguishing features Amy points to are the speed of the software, which is very fast to process large amounts of data and produce large numbers of tables and charts; its in-built handling of time-series, allowing you to combine or suppress periods very easily, and the range of charts offered, in particular the perceptual maps.
Some of the research companies I speak with are becoming uneasy that the legacy data processing tools they depend on have fallen so far behind, and are in some cases, dead products. They have endured because the GUI-based ‘replacements’ at the back of the more modern data collection tools just don’t cover the breadth of functionality that is needed. You get breadth and depth with Ruby – even in the sheer range of functionality it offers is bewildering to the newcomer.
A version of this review first appeared in Quirk’s Marketing Research Review, August 2009.
What it does
Comprehensive desktop analysis software for crosstabs, charts and statistics, with integrated data editing, data processing, presentation and publishing capabilities.
SPSS (An IBM Company)
Ease of use
Compatibility with other software
Value for money
Single user prices: Base SPSS system, £1072, standalone SPSS SmartViewer £132, add-on modules from £473. Annual maintenance and support from £214. Volume and educational discounts available.
- Now cross-platform – PC, Mac or Linux
- Clever data editing including anomaly detection
- Greatly improved charting
- Output directly to PDF
- Wide range of options can be confusing to novice users
- Output can look straggly and utilitarian
This year the statistical software SPSS is forty years old. While SPSS now heavily promotes this program in the so-called business and predictive analytics arena, MR users continue to be well served by the latest issue, SPSS 16. Indeed, there are several very handy new features for questionnaire-based data and the stuff market researchers tend to do.
The big change is that the software has now been re-written in Java. Going to Java has given the developers the opportunity to make a few changes to the dialogue windows – though (before any experienced users break out into a cold sweat) not to where things are or how they work, but in terms of being able to resize items dynamically, stretch windows and see more displayed as a result. It means, for example, that long labels no longer get truncated in selection menus, which has long been an irritation. However, practised users will probably be surprised just how similar SPSS 16 is to recent native Windows versions, considering the interface has effectively been rebuilt from scratch.
SPSS has always been strong in allowing you to edit and clean your data on a case-by-case basis. While there seems to be a recent trend among some researchers not to bother, especially online, those who take these matters seriously should be rather pleased to see this version introduces a heuristic anomaly detector in the data validation menu. Set it going on all the variables you think matter, and it will pull out any cases where the answers stick out from the rest. It uses a clustering, or rather an un-clustering algorithm, and looks for items that don’t cluster. More conventionally, there is also a complete rule-based validation routine, with several handy built-in rules to look for large number of missing variables or repeated answers (mainlining through grids, for example) and the option to set up your own cross-variable checks too.
There are some handy new tools in the data prep area, such as easy recodes that take date and time values and chop them up into discrete time intervals such as months and quarters, or let you group according to day of week, mornings and afternoons and so on. There is ‘visual binning’ which lets you create categories from numeric variables by showing you a histogram of your new categories, and lets you even them out using sliders on screen. A new ‘optimal binning’ function lets you do the same to values, using another variable to determine the fine-tuning of the slices, such as to split income with respect to age.
Version 16 also makes it easier to edit and clean up the metadata – the text labels and names. There is a find and replace feature and a spell checker too, with dictionaries for both UK and US English and for other major languages. The move to Java has made possible other languages and writing systems too, as SPSS 16 now fully supports the Unicode standard.
On the output side, greatly improved charting came in with version 14, and the improvements continue. The visual method for defining charts is one of the most elegant I have seen. Where many tools, like Excel, simplify chart building with a wizard, here the workflow all takes place in the one chart-building window. It avoids the tunnel mentality of the wizard, where you emerge blinking on the other side with no idea of how you got there.
Two items are of particular interest to market researchers. Top marks to SPSS for the ‘panel’ chart option on all the charts, which lets you add a categorical variable such as demographics. It produces neat, side-by-side charts for each category, all the same size and sharing one legend. ‘Favourites’ make it easy to store the chart outline for any chart you have perfected in a gallery for you to use again, saving time and helping you achieve consistency in your reporting.
Behind the scenes, there is also a full chart scripting language, which can be used to automate repetitive chart production. Also of interest to MR users is the new built-in support for going straight to PDF from the output viewer. It offers a fantastic alternative to producing PowerPoint decks merely to communicate data. You can output everything or a selection. Best of all, the complete heading and folder structure of the output viewer is replicated in the PDF as bookmarks, to make navigation easy.
Much of the power and versatility of SPSS has always derived from the ability to write SPSS syntax directly. When you use the graphical interface, the syntax needed to drive the SPSS processor and create your outputs are created for you and can be saved and reused. Advanced users and programmers who use syntax directly will find many more commands and options at their disposal – so it is often possible to create highly customised outputs using syntax. The chart scripting options are just one recent syntax extension. Another intriguing one is a new ‘begin program’ command, which lets you run other external applications and scripts written in the open source language Python. So if the hundreds of statistical tests and models available within SPSS turn out still to be not enough, it is possible to spawn out to ‘R’ (see r-project.org), the open source statistical initiative, and apply any of the hundreds offered in R, using your SPSS data, and presenting the results in your SPSS output.
I was hoping that SPSS 16 would make the program and data structures less disdainful of multiple-response data. In science, and in business, this kind of data is rare, but in market research, multi-coded data abounds. Alas, even in version 16 it is still handled in the same arms-length way through multiple-response sets created from dichotomies. Rather confusingly, there are different multiple sets in the tables and in the special multiple-response frequencies and cross-tabs area. Once you have set them up, there is still that trap for the unwary that they do not get saved in the data, or saved at all without some effort.
My other grumble is that, despite the output improvements, the overall look of the reports that come out is still very utilitarian and is full of irrelevant set-up detail. Cross-tabs in particular are wilfully straggly and unfinished in appearance.
It surely cannot be an issue for the core SPSS users, otherwise you imagine it would have changed long ago, but it is another deterrent to market researchers, where effective communication of results has to be a core strength.
But for the sheer range of statistical tests and models available from one desktop application, SPSS deserves a place in every MR department, agency or consulting practice.
A version of this review first appeared in Research, the magazine of the Market Research Society, March 2008, Issue 501