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It’s Census time again, that means that all 300 million or so Americans get to fill out a survey. The research geek in me loves it (even when the first two questions on the form are poorly designed enough to confuse me briefly). That being said, the New York Times City Room blog had an interesting post highlighting the trivia that is played if you call the Census Bureau and are put on hold.
Try it out, if you call 301-763-4636 you can hear bits on information such as how many Americans have dogs or cats, how many shelled peanuts we eat each year, a 152 year old patent for pencil erasers, etc.
The Times article has some of the scripts and recordings for those of you who don’t want to call and have to ask to be put on hold (or you an call the line dedicated to playing the messages without prompts or talking to people (301) 763 2222).
Any one who has read Freakonomics will be familiar with this story: A University of Chicago grad student does some research in the projects and ends up with a bunch of data about the finances of crack dealers. As it turns out, that was only the tip of the iceberg.
In Gang Leader for a Day, that grad student, Sudhir Ventakesh, tells the whole story. It all begins as a little quantitative survey conducted for one of his faculty advisors. It becomes a sprawling ethnography that takes place over the course of five years, against the wishes of his mentors and his own best judgment. Along the way, Sudhir sees the world inside the roughest projects in Chicago unfold before him in all its complexity. We meet a gang leader who deals crack and serves as a sort of social glue for the community. We meet a community leader who does what she can to help her fellow residents, all while taking kickbacks to better her own circumstances. We meet the police, charged with patrolling the projects, who never show up when called.
Ultimately, it’s a great piece of research, and a story well told.
The Techneos team have started a blog in the past few weeks to publish their thoughts about trends in the mobile survey research world.
We’ve found mobile survey research very useful in the past for studies that are aimed at B2B targets, or highly mobile and/or tech-savvy consumers. With the growth of smartphone and netbook usage, particularly over the past couple of years, this methodology seems more and more vital. It’s great to be able to connect with research participants while they’re on the go, or engaged in their lives (whether for work or play) – not just when they find time to check their email or call back for a phone interview.
We find that expertise in both questionnaire design and data management is absolutely necessary to do in-depth analysis on data collected through this medium. It’s important to be concise in wording your questions and answer options, and you need more than just quick polling data to illuminate insights that lie beneath and behind survey responses. But that’s why we’re here – to recommend a technology solution if and when it’s appropriate for a research initiative, and to exercise our skills and perspectives to take the strategic insights to the next level.
Eric Almquist and Jason Lee of Bain & Company prepared a great interactive presentation on MaxDiff Analysis for the Harvard Business Review this month. This technique is not new to us, but this Adobe presentation is great in clearly communicating the design and dynamism of this technique.
We try to find opportunities to include MaxDiff exercises in our studies that focus on consumer preference. We’ve found this technique particularly useful in segmentation studies where key drivers and motivations for behaviors are of interest.
Including a series of MaxDiff exercises adds to the overall survey length, but this analysis can be very helpful in illuminating how consumers truly prioritize attributes (or features or statements). MaxDiff exercises allow respondents to rate the importance of attributes relative to other attributes, rather than independent of them. The end product is a ranking of attributes that can be used to corroborate key insights gained from Likert scale ratings and factor analysis.
Sometimes the results simply confirm consumers’ stated preference, but often, as shown dramatically in this presentation, a re-prioritization emerges. This is a great way to include survey metrics that enable the analyst to approach the results from an additional perspective. So often, a consumer will rate multiple attributes as about equally important, and through MaxDiff analysis we can sometimes see if some differentiation underlies their response.
There’s also an entry on WikiPedia for Maxdiff, but for more information, contact us!
In conducting quantitative marketing research, analysts have an arsenal of tools and methods that may be employed to develop insight. W5 consultants are constantly developing our analytical techniques [link], and a few of these methods, segmentation and conjoint for example, have become core competencies.
For these types of projects, smart study design is critical, but for the most part these analytical techniques are applied post hoc, in interpretation of trends and spikes that emerge across a large numerical data set. We love this type of work, but we only recommend such an approach if our client’s overall strategic and specific research objectives seem to call for it.
Sometimes, marketing research objectives are best addressed not so much through application of post hoc analytical techniques, but on the front end of the project – through development of a direct, customized, in-depth line of questioning. Read the rest of this entry »
On occasion, we are asked about comparison of quantitative research results to norms – average scores pulled from standardized data points, typically drawn from studies or questions of a similar type or within the same industry.
We do not compile data from our (usually custom-designed) research studies to be compiled into normative databases, but in some cases, we can acquire norms from an external resource to be compared against the results from a study we run. However, we generally recommend against such comparison, as any conclusion drawn does little more than pose a statistical difference, easy to misinterpret and requiring a footnote to explain the foundation upon which it is based.
