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[https://behavioralscientist.org/behavioral-scientists-ethics-checklist/] - - public:weinreich
behavior_change, ethics, research - 3 | id:245235 -

To ensure these partnerships are beneficial to all involved—companies, employees, customers, and researchers­—behavioral scientists need a set of ethical standards for conducting research in companies. To address this need, we created The Behavioral Scientist’s Ethics Checklist. In the checklist, we outline six key principles and questions that behavioral scientists and companies should ask themselves before beginning their research. To illustrate how each principle operates in practice, we provide mini case studies highlighting the challenges other researchers and companies have faced.

[https://link.springer.com/content/pdf/10.1186/s12889-019-6696-2.pdf] - - public:weinreich
quantitative, research, target_audience - 3 | id:244100 -

The aim of this study was to establish if distinct segments were evident in a sexual health context drawing from measures sourced from four segmentation bases extending application of segmentation to all recommended bases [46]. This study indicates how researchers can use two-step cluster analysis to identify segments, which are represented by a group of individuals who share similar characteristics that differ from other groups in the larger heterogeneous target audience. Further, this study demonstrates how available information can be used delivering a dashboard to inform program design and planning.

[https://www.youtube.com/watch?v=LFwSCalhSGY] - - public:weinreich
design, research, target_audience - 3 | id:243694 -

Amy Jo Kim Superfan funnel: 1) Potential customers - who are they? what are their unmet needs? 2) Super fans 3) Job stories - design-ready insights to shape product design - lifestyle and insights re: fans Superfan screener - 3 closed ended multiple choice Q's relevant to topic, plus 3 open ended about what they're doing now, what they want Recruitment via Craigslist, taskrabbit, userinterviews.com, social media ads, friends/family, et al

[https://www.youtube.com/watch?v=efZfRst1gN8&feature=youtu.be] - - public:weinreich
design, qualitative, research - 3 | id:243693 -

20 5-minute Speed interviews with super fans - Amy Jo Kim 1. Day in the life - walk me through the parts of your day with relevant activities 2. What's working with your current approach? What have you already tried to address your pain points? What's working best for you right now? 3. What could be better? What would you change?

[http://www.bhub.org/] - - public:weinreich
behavior_change, design, professional_resource, research - 4 | id:234038 -

Innovative solutions based on how people act and make decisions in the real world are often buried in academic journals. The Behavioral Evidence Hub (B-Hub) brings them into the light of day. On the B-Hub you’ll find strategies proven to amplify the impact of programs, products, and services—and improve lives. Projects + checklists

[https://faculty.wharton.upenn.edu/wp-content/uploads/2012/04/Williams-fitzsimons-and-block-jcr.pdf] - - public:weinreich
behavior_change, research - 2 | id:234030 -

We demonstrate that the mere-measurement effect occurs because asking an intention question is not perceived as a persuasion attempt. In experiments 1 and 2, we show that when persuasive intent is attributed to an intention question, consumers adjust their behavior as long as they have sufficient cognitive capacity to permit conscious correction. In experiment 3 we demonstrate that this finding holds with product choice and consumption, and we find that persuasionknowledge mediates the effects. In experiment 4, we show that when respondents are educated that an intention question is a persuasive attempt, the behavioral impact of those questions is attenuated.

[https://www.sciencedirect.com/science/article/pii/S2352827316301537?via%3Dihub] - - public:weinreich
behavior_change, quantitative, research - 3 | id:226457 -

•Despite its sequential nature, healthcare seeking is often analysed as single event. •We demonstrate the value of sequential healthcare data analysis. •Descriptive analysis exposes otherwise neglected behavioural patterns. •Sequence-insensitive indicators can be inconsistent and misleading. •Sequence-sensitive evaluation hints at adverse behaviours of wealthy patients.

[https://medium.com/dropbox-design/breakups-space-travel-and-design-research-b0a1645724c2?ref=uxdesignweekly] - - public:weinreich
creativity, design, qualitative, research - 4 | id:226318 -

At Dropbox, we’ve found that metaphors are a powerful tool to help people explore and share their experiences in more creative and meaningful ways. We use metaphors in research so people can talk about their experiences through a different lens. We can do this simply by inviting people to make a comparison through a single question. Or we can facilitate entire interviews by using tools to symbolize and explore meaning together.

[https://www.psiweb.org/docs/default-source/2018-psi-conference-posters/48-julie-jones.pdf?sfvrsn=cb68dedb_4] - - public:weinreich
graphic_design, quantitative, research - 3 | id:226195 -

Effective visualizations communicate complex statistical and quantitative information facilitating insight, understanding, and decision making. But what is an effective graph? This cheat sheet provides general guidance and points to consider.

[https://www.news.com.au/lifestyle/food/drink/mountain-dew-just-made-an-epic-advertising-fail-in-scotland/news-story/a3a3576efdc0a0ca56c2bc5d07a0872f#.jhrhv] - - public:weinreich
health_communication, online_marketing, research, target_audience - 4 | id:186610 -

Unfortunately for Mountain Dew The Scotsman didn’t include the fact that “chug” means “masturbation” in this particular part of the UK. And now, as Vice reports, the soft drink brand is being mercilessly ripped on Twitter for inadvertently telling everyone that they’re chronic masturbators. On Monday the company tweeted a .gif of a guy madly downing a bottle of Mountain Dew, with the slogan “epic thrills start with a chug”.

[https://www.genesis-analytics.com/uploads//downloads/Health-2018-BMC_Public_Health.pdf] - - public:weinreich
behavior_change, HIV_AIDS, quantitative, research - 4 | id:177126 -

Typically, cascades are based on HIV treatment moni-toring data, which focus on getting people living with HIVto a point of viral suppression. HIV prevention cascadesfocus on the steps required to prevent HIV infection andsuccessfully implement HIV prevention programs. Preven-tion cascades include demand-side interventions that focuson increasing awareness, acceptability and uptake of pre-vention interventions, supply-side interventions that makeprevention interventions more accessible and available, andadherence interventions thatsupport ongoing adoption andcompliance with prevention behaviours or products...

[https://uxdesign.cc/user-research-is-more-the-merrier-9ee4cfe46c7a?ref=uxdesignweekly] - - public:weinreich
design, qualitative, quantitative, research, target_audience - 5 | id:177113 -

Small, medium or large — what sample size of users fits your study is a composite question. The magic number of 5 users may work magic in some studies while in some it may not. It depends on the constraints put on by project requirements, assumptions about problem discoverability and implications to the design process. Assess these factors to determine the number of users for your study: What’s the nature and scope of research — is it exploratory or validatory? Who and what kind of users are you planning to study? What’s the budget and time to finish the study? Does your research involve presenting statistically significant numbers or inferring behavioural estimates for the problem statement?

[https://www.qualtrics.com/events/identifying-bad-survey-respondents-attention-check-questions/?ty=mktowr-thank-you&aliId=6002] - - public:weinreich
quantitative, research - 2 | id:168142 -

Lesson: Use "commitment" question instead of attention check questions.

[https://sciencebasedmedicine.org/0-05-or-0-005-p-value-wars-continue/] - - public:weinreich
quantitative, research - 2 | id:79662 -

For fields where the threshold for defining statistical significance for new discoveries is P < 0.05, we propose a change to P < 0.005. This simple step would immediately improve the reproducibility of scientific research in many fields. Results that would currently be called “significant” but do not meet the new threshold should instead be called “suggestive.”

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