Tag Archives: SPSS

SAS vs SPSS: Pick one and forever hold your peace #MRX

Any serious researcher will fight you to the death to convince you that their favourite stats program is THE best stats program. But, there are good and bad things about each of them.

SPSS: Great for people who rarely use statistics, who don’t remember code, who are scared by code, have no time to learn code, who just need to use some of the basic processes with no special adjustment, who work with normal data that never changes. It’s quick and easy to learn and use, and you don’t 4 feet of manuals to find what you’re looking for. You can just click through the menu to find all the basics you’ll ever need. Compared to SAS, SPSS is major el cheapo.

SAS: Great for people who love coding. SAS does have a menu version but if you like menus, then you should be using SPSS. SAS is great for data manipulation as in creating brand new variables,  flipping cases into variables and vice versa, and re-running the same bit of code repeatedly with only a tiny change every time. The macros you can write are unlimited and awesome. And, if it doesn’t do the variation of a statistic you want, you can actually program that statistic into SAS. If you like collecting books, SAS can quickly contribute to that addiction. Buy them. You will need them.

R: What? A third choice? Oh yes. R is great for people who want to flaunt how anti-establishment they are. It’s open source and requires a lot of commitment to become competent. But it can do any statistic you’ve ever dreamed of and a billion more. And you can brag that you know hard core statistics programming. Do not attempt to learn it if Excel intimidates you. Otherwise, go and download it right now and get ready for a rip-roaring awesome 6 week holiday. It is free so prepare to salivate. Mmmmmm…. rrrrrrr. There are even a couple of self-help books now so you might want to find one of them. So, if you’re fresh out of school and no longer have the student version of SAS, R will be perfect for you.

What’s my preference? Well, I wish I was competent in R, but until then, SAS is the best thing since sliced bread.

Does your market research supplier offer a conjoint product? I hope not. #MRX

FactorAnalysis ConceptualModel DotsRings

If you visit the website of any market research company, large or small, full service or end service, you’ll find an array of product offerings.

Below you see the product offerings from two different companies. Company A gives an impressive list of everything from conjoint to perceptual mapping. Any company offering these products is sure to be able to meet all of your market research needs, whatever they may be. Company B also provides an impressive list of offerings ranging from In Home Usage Testing to sales forecasting. This too is a company that I know will be able to meet whatever needs I may have.

Product offerings: Company A

  • Conjoint analysis
  • Factor analysis
  • Cluster analysis
  • Maximum difference scaling
  • Perceptual mapping
Product offerings: Company B

  • IHUT
  • Package design
  • Simulated shopping
  • Concept testing
  • Media testing
  • Competitive analysis
  • Pricing analysis
  • Sales forecasting

But really, only one of these companies appeals to me and it’s not Company A. Clearly, Company A has licensed a copy of SPSS or SAS for every one of their employees and that does impress me. But it’s clear that those employees are number crunchers and data processors and that’s really not what I’m looking for. You see, conjoint isn’t a product. Factor analysis isn’t a product. Maximum difference scaling isn’t a product. Those are buttons you press in SPSS.

The company I AM interested in Company B. They clearly know that clients aren’t looking for a really cool conjoint analysis or a wickedly awesome factor analysis. I seriously couldn’t care less if a supplier offered conjoint. What clients actually need is guidance on pricing decisions, package evaluations, and concept preferences.

You see, anyone can press a button in SPSS and generate pages and pages of output. But to actually apply those results to a meaningful business decision, well that’s a separate story.

SPSS Releases A New Extraordinary Version #MRX

spss screen capspss screen capspss screen capspss screen capIt’s always a happy day when you get your hands on the newest version of software. Today, I get to show you some of the features that will be upcoming on the next version of SPSS. Hold on tight because us researchers are in for a real treat.

First of all is a new feature in the Edit section. Normally, we find ourselves going through an entire set of data, an entire analysis of the data, only to realize we discovered absolutely no insights. Have no fear. Just choose the “Insert Insights” button before you start your analysis and you’ll be sure to discover a variety of valuable insights throughout your analysis.

Second, though a lot of research projects are very interesting to the client and the brand manager, they’re often not very interesting to the researcher. Sure, I use toilet paper, but I just don’t find it to be an exciting category except when I unpleasantly discover we’re all out. Well, SPSS has solved that problem too. Just choose the “Recode Into Interesting” button under Transform, and the data will now be fun and interesting to analyze. I do not believe, however, that it will restock the toilet paper.

Third, we know that a picture is worth a thousand words. We also know how much effort and design skill is required to turn pages and pages of numbers into interesting and meaningful images. Thanks to SPSS, now all you have to do is choose the “Cool Infographic” button under Analyze/Descriptives and all that hard work is done for you.

Fourth, finally, and probably the best feature of all. We all know the disappointment of conducting an entire research project only to find that the most important research result was only significant at p=0.06. Alas, never worry about this terrible problem again. Just choose the “Make Significant” button and the result will become significant to the next closest break point, whether that means 0.05, 0.01, or 0.001.

Thank you SPSS for letting me review your next release. I can’t wait until everyone gets to use it!

Inspiration for Researchers: Joy #MRX

After a long hard day of surveying, dataing, coding, and focus grouping, everyone needs a little inspiration to keep them going. This is for you.

MARKET research joy

Really Simple Statistics: p values #MRX

Welcome to Really Simple Statistics (RSS). There are lots of places online where you can ponder over the minute details of complicated equations but very few places that make statistics understandable to everyone. I won’t explain exceptions to the rule or special cases here. Let’s just get comfortable with the fundamentals.

What is a p value?

P value is a short form for probability value and another way of saying significance value. It refers to the chance that you are willing to take in being wrong. (I know, once in your life is too many times to be wrong.)

No matter how careful you are, random chance plays a part in everything. If you try to guess whether you’ll get heads or tails when you flip a coin, your chance of guessing correctly is only 50%. Half the time, you’ll flip tails even if you wanted to flip heads.p value

In research, we don’t like 50/50 odds. We instead only want to risk that 5% or 1% of our predictions are wrong. And, if you just  picked 1% or 5%, you’ve just picked a peck of picked peppers. Whoops, I mean you’ve just picked a p value.

P values are almost always expressed out of 1. For example, a p value of 0.05 means you are willing to let 5% of your predictions be wrong. A p value of 0.1 means you are willing to let 10% of them be wrong. Don’t let that pesky decimal place fool you. A p value of 0.01 means 1% and a p value of 0.1 means 10%.p value

When you do a statistical test in software like SPSS or Systat, it will tell you the exact p value associated with your specific set data. For instance, it might indicate that the p value of your result is 0.035, or “Men are significantly taller than women, p=0.035.” That means there is a 3.5% chance that men are NOT actually taller than women and this result happened only because of random chance.

Really Simple Statistics!

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