Thanks for sharing this

Excellent presentation! Tysm for sharing

This is very helpful presentation for qualitative data analysis.

How may I gain access to your presentation slides??

Always a pleasure, thank you Research Rockstars / Kathryn

Thanks, this is very informative, how about the pdf diagram you promise to share. Thank you.

Excellent and thank you

Amazing video Kathryn.

I wish you a lot of success on your channel!! You should research "PromoSM"!!!

Is this career lucrative?

ρɾσɱσʂɱ

Hey everyone, also do check on how QR Codes can help in increasing survey response: kzbin.info/www/bejne/lZSuhqaBh9-Lhac

Thank you.

This career looks very interesting and exciting but I’m curious to know how stressful is this job? Are the hours long or a regular 40 hour week? Work/life balance? Etc.

Good video! One suggestion to round out the round table is to get someone who is currently client side doing insights

Very good - thank you

Thank you for this PowerPoint template gift!

These companies want to combine CI and BA but still pay below market salaries 😂

Hi Kathryn very nice presentation... would you mind to share the name/address of the tool that you have used? many thanks!

Informative and well-organised. Yet doesn't quite match the title.

Really useful overview of the pros and cons of different structures here, thanks!

Thanks a lot!

Very informative video.

Incredibly helpful, thank you very much for the insight!

GOLD.

For more info about trending market please visit - Mostmarkets.com

I’ll take R over SPSS any day of the week! Data analysis and visualization with flexible code to make changes on the fly and to easily document for others for future use. People are just lazy and don’t want to learn the basics of code. Great channel - much needed!

Very insightful. Thanks 🙏

Thank you so much for this. It was really helpful

Hmmm, very informative. I would have loved a more practical aspect of the visual representation. Good work all the same.

Thank you for sharing this information. Much helpful. Can you help with the PDF link for the framework, that you mentioned in the beginning of the video? Thanks.

thank you! this helped me so much with a paper I was writing

I really appreciated this content! I'm doing a master's program in Design and I research a particular product, the cap (derived from hats). Along side the historical background of the product (the baseball context which he comes from) I research on data to find out what's the meaning of the cap today. I'm focusing on Twitter data and figuring out methods to deal with it. Sentiment analysis seems to be one of the most used in terms of big data derived from social medias. I wonder the possibilities of research beyond textual resources. The internet it's full of communication based on visuality; emojis, memes, short videos, and goes on and on. I just discovered the channel. Good work!

Thank you so much. Subscribed!

Hello! Great video. Thanks :)

Best thanks

Finally, I saw you 🙂 great training thx

Thank you for the breakdown. I appreciate the line drawn on Social Media Research - it could easily fall into Qualitative.

Excellent thank you

Great conversation, thanks for sharing!

Good video - short, clear, and to the point. Yes - large sample sizes will not compensate for poor data quality. Rigor is healthy when it comes to scale construction and indirect questioning.

Here's the email invitation research link: www.researchrockstar.com/CompendiumofEmailSurveyInvitation

Here is the link to the PDF mentioned in the podcast: bit.ly/2DPkmaL

Everyone looking to find a new job using their market research skills needs to watch this video by Research Rockstar! Extremely helpful

this is very insightful thank you

GREAT!!!!!

Researchers creating surveys need to always be thinking of the respondent's experience. Many survey takers don't bother to read long, wordy questions. They just skip to the answer scale and read the anchor points. In addition to reducing bias, concise questions keep respondents engaged. Semantic differential scales work well for respondents who don't read instructions.

Semantic differential scales are good but challenging when you have more several brands you want to rate.

Likert not Laykert