Steven Jasewicz’s Post

🔍 📊 The incidence rate is pretty important in market research yet many still find this concept a bit unclear. James McClain, Director, of Account Management helps to break it down! What is incidence rate? The incidence rate is the percentage of people in a defined target population who qualify for a particular survey or study. In simpler terms, it’s the proportion of individuals who meet the criteria to participate in the research. This can vary greatly depending on the study's type and objectives. Why does incidence rate matter? 1. Sample Representation 2. Cost and Efficiency 3. Data Quality 4. Statistical Significance Read the blog to learn more about incidence rates💡https://okt.to/w0xB5W #marketresearch #incidencerate #dataquality

Aiming for a decent Incidence rate in Primary Research data collection is not just about Project feasibility, time, cost and margins. An important but neglected dimension of Incidence Rate is the Representativeness of the data. Read this blog to know more about this!

An Incident of Incidence Part 2: Pricing There's a growing discussion in the research community that pricing should not be influenced by incidence rates. Researchers prioritize completion data over incidence/term data. Read more in this blog : https://lnkd.in/gfGP2Kaa #ResearchCommunity #IncidenceRates #PricingStrategy

I’m not sure what you mean with the incidence rate. 🤔 This is the most common reply I get,  when I am asking someone what the incidence for a target group would be. Incidence rate is one of the most important metrics in an online survey as feasibility and costs are affected by it. Therefore an accurate estimation of the incidence rate will determine the success of your research. ✅Basically, we calculate the number of qualified respondents vs total number of respondents. ✅A low incidence rate implies that more people need to be screened to find enough qualified participants, potentially increasing the cost and time required for the research. For more information about this subject please feel free to check the article witten by James McClain from Dynata or contact me to help you further. 🚀 #dynata #marketresearch https://lnkd.in/d4JaRD5t

Searching for relevant data that’s formatted and ready to compare? CIHI’s national health expenditure data comes pre-formatted and ready for analysis the moment you download it. Discover the data you’ve been looking for and optimize efficiency in your role: https://ow.ly/tomH50P3OFM

Searching for relevant data that’s formatted and ready to compare? CIHI’s national health expenditure data comes pre-formatted and ready for analysis the moment you download it. Discover the data you’ve been looking for and optimize efficiency in your role: https://ow.ly/tomH50P3OFM

📢 Do you have a question for our European Omnibus? Norstat Quarterly Data Q1 2024 is out, and you now have the opportunity to include your questions in Norstat Quarterly Data Q2 2024!   🌍 Every quarter, we publish the results of a survey carried out in 15 countries across Europe. With Norstat Quarterly Data, you receive current data on our three recurring themes: Society, Consumer and Health & Wellbeing. Find out more and get access to the data: https://lnkd.in/dMpwuAmc   Couldn’t find the answers you were looking for in the report and dashboard? You can now add questions relevant to you in the next wave of Norstat Quarterly Data.   🚌 Join the European Omnibus and compare answers to your questions across markets: https://lnkd.in/dRDzJ4dS #marketresearch

Title: Covid-19 Global Analysis Data source: World Health Organization website Tool used: #MicrosoftPowerBI Insights: • Death count by Country • Yearly case trend • New cases and deaths • Case count by region #analyticsjourney #analyticsinsights #dataanalytics #dataanalysis #datacleansing #datacleaning #datainsights Quantum Analytics NG

🔢 Understanding Margin of Error in Data Analysis 🔢 When analyzing data, the margin of error helps us understand how much our sample's results might differ from the actual population. It’s a critical concept in fields like marketing, polling, and A/B testing—essential for making informed decisions. 📊 Key terms to know: Confidence Level: The percentage indicating how likely your sample reflects the larger population. Population: The entire group you’re interested in. Sample: A subset of the population, used to estimate results for the whole group. For example, in A/B testing for marketing campaigns, if subject line A has a 5% open rate and subject line B has a 3% open rate with a margin of error of 2%, it’s important to recognize that the real open rate for subject line A could be anywhere from 3% to 7%. This overlap may mean no significant difference between the two. 🎯 Using a margin of error calculator ensures your data interpretations are accurate and reliable. Remember, the margin of error is crucial for interpreting results and drawing valid conclusions from your data. #DataAnalytics #MarginOfError #ABTesting #ConfidenceLevel #DataScience #DecisionMaking https://lnkd.in/gU92EmxR

As another small but hopefully mighty upgrade in the data book, we included two new fields that we were forever having to manually calculate in previous books. The ratios per 10,000 population were historically trapped in the data book maps, and you had to do the math to get specific values for each county. Now we list it for you. We also added a percent of state total value. As super users of the data book, we kept writing in values—like the percent of family docs who were in rural counties—and we'd have to run the data to more easily identify that 73% of SC's psychiatrists are in 3 of its 46 counties (where 26% of the state's population lives). The math isn't that hard, but we hope it'll make your work just a bit easier.