However, the misrepresentation of research statistics is another big issue common to drug advertisements. Let's look at some examples.

1. Dacogen
Used to treat some rare blood cell disorders and cancers, drug manufacturer Eisai claimed in a patient brochure that 38% of study patients responded positively to the drug. In a November 2009 letter from the FDA, the study's claims were said to be false. The FDA said the 38% figure was misleading because it was "...taken from a small subgroup of patients who responded well to the drug. Including all the patients in the study, the response rate was a mere 20%."
When reviewing statistical results, it is always important to consider the population from which the sample came from and to compare the statistical results to additional trials or previous related research. This study falsified the generalizability of the statistical results from one subgroup of the sample to say that the results could be expected for many people.

2. Kaletra
Kaletra is an AIDS drug from Abbott Laboratories. The company came under FDA scrutiny after a testimonial DVD featuring Magic Johnson suggested that the drug could be helpful to most HIV patients in managing their illness. In a July 2009 letter, the FDA warned the company against such claims as in a clinical trial, the drug was shown to be ineffective for 37% of patients.
This example is a reminder of the importance of knowing a research study's methodology. While the drug was ineffective for nearly 40% of participants in one study, the drug company overstated the drug's effectiveness and generalizability with the omission of critical information, such as the overall sample size, population from which the sample was drawn, and subsequent statistical results.

It's no surprise that companies and retailers all over are investing more resources into tracking information posted on social media sites. What is surprising is just how useful this data can be; for a very low cost, companies can track valuable information like what consumers "like" and what they're searching.

As the above Times video shows, companies can combine this information with personal user information like name, age, gender, location, photos, etc. to create a more unfiltered profile of a consumer than would typically be gathered in a focus group setting.

Using social media as a sort of replacement for focus groups has proven particularly rewarding for companies because they get uncensored feedback and results. These unfiltered comments and results eliminate some concerns that are typical in traditional focus groups, such as respondents not having enough opportunities to express their opinions, expressing opinions that are not their own in effort to appease the moderator, or to not be in opposition with other members of the focus group. Social media also allows companies to get a lot of information from younger consumers, an age group that typically doesn't engage in focus groups.

As social media usage increases, market researchers will undoubtedly continue to exploit the information posted on these sites. It will interesting to watch how the development of social media encroaches on traditional research methods.

It all began when the Australian Department of Transport and Main Roads decided to implement a survey of bus stops along James Cook University in order to gauge how many people were using the buses along the route; the results showed that the bus stops were being grossly underused, which resulted in the decision to cut services to these stops in order to protect the financial resources used to maintain these routes.

However, Sunbus, the company that ran the survey on behalf of the Australian Department of Transport and Main Roads, failed to communicate with University officials when deciding when to do the survey. As it turns out, the survey gathered data from a non-representative sample; the sampling time frame that was used happened to be exam week at the university, a time, as we all know, that significantly reduces traffic on campus since students tend to either be at home or in the library studying.

This real-world example of misleading survey data should be taken as a lesson of the importance of accurate sampling and effective communication in research. When conducting surveys, it is highly important to select a sampling time frame that will reflect a "normal" event. It is also important to consider whether one survey sampling time is adequate; in this instance, if more than one survey had been conducted at different time intervals, it is likely the data from the exam period would be have been considered abnormal, or outliers, instead of the norm.

Just as it is important to select representative sampling time frames and intervals, it is equally as important for clients to communicate effectively with all parties involved in survey data. Had the research company spoken to University officials, they would have been informed about the dates of exams and could have a chosen a time that more accurately reflected day-to-day usage of the buses. This one oversight led to a waste of government monetary resources and could affect all students and teachers that rely on campus bus stops for safe transportation.

For full news coverage, click here.

2010's top 3 most popular methods of reaching multicultural consumers through new media:
1. The company's website (75%)
2. Online ads (72%)
3. Search-engine marketing (71%)

It's interesting to note that the use of other new mediums to reach speciality populations are rapidly growing; 32% of respondents said they used location-based apps in 2012 to reach multicultural segments (compared with 2% in 2010), the use of blogs has increased from 27% in 2010 to 44% in 2012, and 64% reported using mobile marketing (59% in 2010).

These new trends indicate that Internet and GPS technologies will continue to make special population sampling easier and more efficient. GPS technologies are particularly useful as the people who reside in a geographic area tend to be similar to each other in many ways, particularly in socio-economic status and culture. Blogs and mobile marketing are also proving useful methods of reaching smaller populations as they can create a sort of "speciality environment" where members of a group congregate either on the same blog or through similar applications of their mobile devices.

These findings are particularly important to market research so as to increase response rates; the more effectively marketers are able to target their populations, the more likely it is that the participants will respond since the information is truly relevant to them.

Their blog is mostly loaded with market research experiments done by Brain Juicer and other companies, but also occasionally throws in industry-related cartoons and ads.

One of the entries I found most interesting and relevant to our own course material had to do with the effectiveness of click-through advertisements. Citing an article originally posted on AdAge, the Brain Juicer post "I belong to the Blank Generation" details an experiment that measured the amount of click-throughs on 6 blank ads, and then compared the number of click-throughs to those of other branded ads.

What the researchers ultimately found was that the click-through rates for the blank ads did not vary significantly from the click-through numbers for actual branded ads. This finding then raised the question, are click-through ads reliable metrics of online behavior?

To make sure the results were accurate, the researchers used various methods to detect any potential click fraud; these methods included tracking "...hovers, interactions, 'mouse downs,' heat maps--everything. (Heat maps detect click fraud because bots tend to click on the same spot every time.)"

The results suggested that roughly 4 clicks in every 10,000 impressions are unintentional. The research also indicates that all the online noise serves as a confound to the reliability of click-through rates as a metric. The extra noise encountered online can lead users to mistaken clicks, thus distorting the representation of intended behaviors and rendering behavioral data almost unidentifiable from surrounding noise.

Brain Juicer sums it up best: "We are great believers in focusing on behaviour, and that changing behaviour should be a research outcome. But - especially online - there is an awful lot of tempting behaviour to measure, and it's easy to be seduced by that. 'If you can't measure it, you can't manage it,' the gurus tell us, and they sound very pragmatic. But it doesn't make 'If you can measure it, you can manage it' any truer. A click seems concrete, but may be as insubstantial as... a blank advert."

These findings further reinforce the importance of working backwards in research; it's more important to focus on data application than it is to focus on data acquisition. Just because there is data to measure doesn't mean it's reliable, or even applicable to any business objectives.

Greenbook recently hosted a webinar regarding upcoming industry trends in 2012.

Highlights include:

-Marketers are trending toward "do-it-yourself" research (like Survey Monkey)
-Analytics are becoming marketers' primary focus and are becoming highly integrated
-Qualitative and quantitative research are becoming less distinct and they will continue to merge with qualitative items being included in quantitative methods (like surveys) and vice-versa
-Mobile surveys will continue to gain popularity among consumer-oriented surveys and as so, will lead to shortened survey lengths and more streamlined research objectives across all mediums/projects
-On the other hand, it will become increasingly important to consider the results of other survey sources as mobile and social media research can lead to biased results towards specific demographics and behavioral audiences

With mobile usage ever increasing, it was only a matter of time before mobile survey apps became a new trend. Here, we look at the pros and cons of mQuest, one of the leading mobile survey apps.

Pros:
-The survey can be corrected in realtime, even when it is ongoing, to control for any errors or confusing items that may be discovered.
-Surveys can be supplemented with embedded visuals, audio, and videos.
-Surveys can be translated into multiple languages, allowing for diverse samples of people to participate.
-Built-in "plausibility checks" to control for wrong inputs.
-Fast (information is saved as survey is taken) and FREE.
-Survey questions can be made in many different formats.
-Works for both Android and Apple devices.

Cons:
-Limited amount of questions that one can ask on a mobile device.
-Questions on a mobile device cannot be too complex since people are on-the-go.
-Not everyone owns or has access to a mobile device.

Overall, mQuest seems to have more advantages than disadvantages, however, researchers must make sure that mobile surveys actually make sense for the data they are trying to collect. Just because mobile technology is a growing medium doesn't mean it is the right medium for all research studies.

mQuest's website

The Times article details why observational studies are usually hard to trust for reliable results; it mentioned issues with observational data we have discussed in class, such as confounding variables and the lack of randomization. The interesting part is that although the Times mentions these common observational issues, the article advocates for the study's results since experimental and longitudinal studies have found similar results, yet these have been ignored for the past decade.

The Times article states, "It is normally troubling to see an observational study posing questions asked and answered by higher science. But in this case the research may help society to emerge from a fog that has clouded not just the approach to data on screening mammography, but also the approach to health care in the United States. In a system drowning in costs, and at enormous expense, we have systematically ignored virtually identical data challenging the effectiveness of...cancer screening...and more."

Overall, the article makes a good point that when the results of experimental methods and trials are ignored, observational methods may be able to help break socially accepted "fact." Although experimental methods do control for issues such as confounds and randomization, in turn providing more reliable and generalizable results, observational research may prove to be a good supplement by providing a less scientifically-laden, and more understandable, methodology and approach.

As a sleep product company, it was hard to engage with specific users since there's no directory of people based on their mattress brand, nor is there a list of consumers who have brought new mattresses in X time period. However, in order to grow the company and make new products, this is exactly what Sleep Innovations needed to do; they needed to reach a hard-to-reach segment to uncover new user insights through consumer's attitudes and usage towards sleeping products.

Knowing that this segment would be hard to reach, Sleep Innovations created online surveys to pre-identify the brand's purchasers and purchase intenders. This extra work out front in pre-identifying their sample ended up saving time and money in the execution of the company's actual "Sleep Talkers" panel community.

The panel community has been successful in gaining consumer insights as the pre-selected consumers find the information relevant and are encouraged to take photos of their "sleep experience," post on community discussion boards, and are invited to partake in more traditional research methods, like concept testing and new product screening.

Sleep Innovations even developed an additional online survey template that was able to track responses over time. In one instance, this additional survey led to an important insight that one of their mattress brands was not selling well in a certain region because the brand's perceived image was too high for its low price; Sleep Innovations raised the brand's prices and watched sales increase.

Overall, Sleep Innovations was able to successfully reach their "hard-to-reach" population by implementing a step-by-step approach that used custom screening to pre-identify the users for whom this information would be the most relevant and interesting, prioritize the actual research activities over the company's own product development goals, establish clear research objectives for the panel to get the most directed feedback, and use visuals to gather feedback.

Check out the whole case study here.

With this survey particularly, it was smart of the survey designers to write in the body of the email, and in capitalized letters, that all information is confidential. Usually I read about confidentiality in the informed consent section prior to taking a survey, but in this instance, if the confidentiality tidbit hadn't been entered in the email's body, I probably wouldn't have even clicked the survey to read the informed consent.

The content in the survey was also created well, specifically its use of many filter questions to filter out those respondents for whom an item would not be applicable.

Also, this survey's use of incentivizing (what college student wouldn't want a $25 Target gift card?) was especially well done. If you require that your respondents provide sensitive and personal information, a little incentive can go a long way.

The night before the election, CBS news posted an article declaring that based off of polling results, the election was sure to be close, "a presidential race teetering on a knife's edge." Citing polls by CNN/Opinion Research, USA Today/Gallup, and the final survey from WMUR/University of New Hampshire, CBS news all but promised an election with extremely close results.

However, CBS News' outcome article the very next morning told a different story. The article began, "In the end, it wasn't close. Barack Obama won re-election handily over Mitt Romney with 303 electoral votes, well more than the 270 electoral votes needed."

These conflicting articles, from the same news source, each released with 24 hours of the other, is is a good example of how it is necessary to exercise caution when examining poll and survey research. Even when we think we have been thorough in our samples and have cross-checked with other news sources' findings, it is important to remember that polls and surveys are only a general estimate of public opinion and very well may not reflect the true outcome(s).

The Institute for Public Relations (IPR) is "An independent nonprofit foundation dedicated to the science beneath the art of public relations™. We focus on research that matters to the practice, providing timely insights and applied intelligence that professionals can put to immediate use."

The IPR tries to achieve these goals by focusing on:
1. Social media research that provides insight into how to best guide and evaluate public relations (PR) communications
2. Research on how to understand the best PR practices (what to do and how to do it)
3. PR research as "the social science underpinnings of our work"

Since social media has begun to play an ever-increasingly important role in day-to-day life, companies are more concerned with figuring out what drives customer behavior, and how to apply what the social sciences have discovered about behavior in order to further PR efforts.

For a free research center, IPR offers a lot of valuable information. The site hosts articles tracking social media use in PR for the past decade and detailing how big organizations use social media to stay connected to their customers. There's even an article dedicated to an analysis of how Fortune 100 companies use Facebook in PR efforts.

With this sort of information emerging online for free, it will be interesting to track how new technology will shape the delivery of content to customers based off of those same customers' preferences and behaviors. It's easy to imagine just how specific and targeted PR efforts will become. Are the last days of expensive, formal and experimental research lingering? With the recent advancements of online tracking and other social media/online-based research methods, one simple Google search can lead to resources providing a basis for understanding important usage patterns for customers and competitor companies alike.

For more information, visit the Institue for Public Relations.

As obesity rates in America continue to climb, so does the importance of exploring the possible long-term implications of such an epidemic. Researchers at the Washington University School of Medicine were interested in discerning why some overweight people develop health problems like diabetes and hypertension, while others don't.

No longer satisfied with using rodents as research subjects, after all there are myriad differences between the biological mechanisms of humans and mice, these researchers decided to conduct a unique, and somewhat controversial, experimental study to gain better insight into the effects of weight gain on human beings.

Using a sort of convenience sampling, researchers put out ads looking for participants who were willing to gain 5-6% of their body weight during the course of the 3-month study. Selected participants had to eat an extra 1,000-calorie meal once a day at 1 of 5 selected fast food restaurants (Taco Bell, McDonalds, Pizza Hut, Burger King, and KFC). Since fast food chains regulate and disclose the nutritional content of their meals, this was an inexpensive and easy way to moderate the participants' extra caloric intakes.

Although participants could receive compensation up to $3,500, and help losing the weight after the study concluded, ethical concerns arise over the research standard of beneficence. Do the monetary benefits really outweigh any potential physical health problems that may arise from gaining so much weight so rapidly?

This is a question that has driven much media attention around this study.
Individuals in the study quickly found out that it is unpleasant to gain weight rapidly; it's no easy feat to constantly eat as your body's natural functioning tells you to stop. Participants complained of breathing difficulties and physical pain as a result of the dramatic and fast weight gain.

While it's inarguably important to track the effects of social and health trends, such as obesity, at what point is the cost to participants greater than the benefits to science?