From the Help Desk – Population Data Filter

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Hello readers! We hope you are having a wonderful day. Today we feature a question from the help desk that we think many users would find useful. Let’s get started.

The question: How can I identify all cities in the US that have a population of 25K to 100K?

The answer: This is a great question, and a perfect use case for the data filter feature in SimplyAnalytics. Here’s how to do that: In your Project, select New View (1) > Create Ranking Report (2)

next, set your Location to: USA (3) – then add in the Total Population data variable (4) that is found under the Popular Data Category. Click Done (5).

At the top of the Ranking Report, set the dropdown to be “Cities” such that it is displaying: Cities in USA

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Variable Menu Improvements & More

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Hello readers! We hope you are enjoying the holiday season as much as we are. Today, we are happy to announce a couple of improvements to SimplyAnalytics designed to help you get your research done more efficiently. Let’s check it out!

Variable Menu Changes

This first of two changes to the variable menus is rooted in customer suggestions that revolved around the same sort of question, namely: is there a way to quickly add in additional years or the # or % version of this variable? As of this week, the answer to this question is yes!

Now, when you are in a report and want to add in other years of data quickly, simply click on the variable name and refer to the Add Other Years text in the menu that displays.

In the example below, the chosen data variable is # Hispanic or Latino Population for the year 2021, but notice in the Add Other Years option, you are now welcome to instantly add in years from 2015, 2016, 2017, 2018, 2019, 2020, 2025 and 2026.

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Introducing Histograms to SimplyAnalytics

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Welcome readers! Today we are excited to welcome histograms to SimplyAnalytics. We hope you enjoy this wonderful new visualization as much as we do. Let’s take a look.

What is a histogram?

In short, a histogram is similar to a bar chart, but instead groups values into ranges (referred to as “bins”). The height of each bar displays how many values fall within that bin.

Example

Let’s take a look at an easy to read example. The histogram view can be found under New View > Histogram in the program. The example below is showing the % of adults (pop 25+) with a bachelor’s degree or higher in States in the USA.

 

Each bin size, or bar width along the X axis represents a 5% range – so, 20-25%, 25-30%, etc. (zoomed in below)

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SimplyAnalytics Swag-a-palooza for ALA Attendees!

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Hello (virtual) ALA attendees! Thank you for dropping by our booth to learn more about SimplyAnalytics. Please email us if you have any questions or would like a live demo or free 30 day trial of our database. We are always happy to chat.

This year, we have two awesome Swag items for those who participate in our sweepstakes.

Our GRAND PRIZE is a $250 Amazon Gift Card!

And ten lucky runners-up will win their choice of SimplyAnalytics socks! 

How to Enter:

There are two ways to enter:

  1. Follow us on Twitter: https://twitter.com/SimplyAnalytics 
  2. Like us on Facebook: https://www.facebook.com/SimplyAnalytics

OR you can follow and like us for two entries in the sweepstakes!

Winners will be randomly selected from our new “Twitter followers” and “Facebook likers” who follow and like us between June 22nd, 2021 and June 28th, 2021.

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SimplyAnalytics Exhibiting at the Virtual ALA Annual 2021 Conference – June 23-26

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Hello readers! SimplyAnalytics will be exhibiting at the Virtual ALA Annual Conference this year on June 23-26. Be sure to drop by our booth, and click the banner image to video chat with us! We’ll be available during the hours below providing demos and answering any questions you may have about SimplyAnalytics.

Also, don’t forget to click on the Swag-a-palooza link for your chance to win some socks or a $250 Amazon Gift Card!

Exhibit Hours

Wednesday, June 23
10:00am – 5:00pm

Thursday, June 24
10:00am – 5:00pm

Friday, June 25
10:00am – 5:00pm

Saturday, June 26

10:00am – 3:00pm

 

 

 

 

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Introducing Simmons NCS Crosstabs

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Hello readers! We hope you are doing well and staying safe. Today we are excited to introduce the Simmons NCS crosstabs functionality! This is a feature we’ve been working on for a while, and we know many of you out there have requested it. The wait is officially over, and this data/feature is now available for trial or purchase right now. Let’s dig into it.

What is a crosstab?

A crosstab is a table showing the relationship between two or more data variables. This is extremely useful in identifying correlations, patterns or trends when working with large datasets.

What exactly does that mean?

This means you can instantly look up to see if there is any relationship between age groups and social media patterns. Or trends between income and the types of sports people watch on TV, or the types of vehicles owned relative to exercise habits. Because we use the Simmons NCS data, there are literally tens of thousands of demographic and consumer behavior variables you can research! 

Why should I be excited?

Traditionally, doing this type of analysis is tedious and requires users to be well versed in advanced data analytics platforms that require a lot of time and effort to learn. In SimplyAnalytics, however, we’ve developed this feature to enable all users to run crosstabs in a few mouse clicks!

Show me!

In SimplyAnalytics, click on New View > Create under the crosstabs report. This opens the Edit View screen. By default, the USA will be selected as the location since Simmons NCS data is only available at the national level.

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Introducing Scatter Plots

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Hello readers! We hope you are doing well, and thank you for your continued support of SimplyAnalytics.  We are excited to announce that scatterplots are officially live! Scatter plots are a great way to visualize the relationship between two different data variables, and we know you will enjoy them as much as we do.

Let’s take an in-depth look at this new feature.

What is a scatter plot?

A scatter plot is a graphical representation where the values of two data variables are plotted along the x and y axis. Each dot represents both the x and y values for a single location, such as a ZIP Code or county.

Why use a scatter plot?

Scatter plots enable users to identify correlations between two different variables. Let’s take a look at an example below using SimplyAnalytics where we’ll use the % of Adults (25+) with a college degree and Median Household Income to see if there’s a correlation between the variables for Counties in the USA.

Getting Started

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A COVID-19 Analysis with SimplyAnalytics

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We find ourselves in the middle of an unprecedented crisis the likes of which most of us have never experienced before, and hopefully won’t have to experience again. There is the occasional moment that makes us smile in the middle of it all (like when people are buying up all the fluffy yellow baby chicks out there – but please, don’t cuddle your feathered friends), but overall the situation is scary and it’s probably safe to say all of us are feeling anxious about the near and distant future alike. The reason we’re so concerned is not just because of the scale of this outbreak and the impact it’s having on individual people, society, and the economy, but also due to uncertainty and confusion. We don’t yet know much about this novel virus, and that’s why everyone is trying to collect and share as much information as possible. To that end, we want to provide some insight into the spread and severity of the Coronavirus using health and demographic data.

Aside from actual virus spread data, we can look at various factors that might indicate elevated risk of developing severe illness if and when the virus spreads in an area, i.e. who will need extra help recovering from COVID-19, the lung disease caused by the Coronavirus. These risk factors are most reliably described by national and international health organizations such as the CDC, the WHO, and the ECDC. And although these sources do emphasize that we don’t yet know for sure what sort of factors actually affect our susceptibility to the virus and severe progressions (for example, we do see young, healthy people becoming very ill), they provide what amounts to their current best estimate.

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COVID-19 Data is Now Available in SimplyAnalytics

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As COVID-19 spreads across the United States researchers need access to the latest data visualization and analytics tools. As a result, the SimplyAnalytics team just added COVID-19 data at the national, state, and county levels to SimplyAnalytics. The data is from USAFacts.org and we update it daily with the latest numbers.

The following data variables are now available in SimplyAnalytics:

  • Confirmed COVID-19 Cases by date
  •  Prevalence of confirmed COVID-19 cases (per 100,000 individuals) by date
  • Deaths from COVID-19 by date
  • Prevalence of deaths from COVID-19 (per 100,000 individuals) by date

You can find the COVID-19 data in SimplyAnalytics in two places:

  • [recommended] Go to the Data tab, click on the “Data Folder” view (located just below the data search box), then scroll to the bottom of the list of data folders, or
  • Go to the Data tab, click on the Health category icon, then check the box next to COVID-19 in the list of health sub-categories

This data can be combined with our demographic and health data variables to identify areas with vulnerable residents, such as the elderly, people with respiratory illnesses, and smokers.

Here are the best ways to view this data in SimplyAnalytics:

  • Map View: map the COVID-19 data by County or State
  • Ranking View: rank all counties in the United States to identify areas with the highest counts or rates of COVID-19
  • Comparison Table View: add your counties and states and any COVID-19 variables to this report to compare rates over time
  • Related Data Table View: add one variable from a folder to this view and all of the rest will be added automatically

Here are two examples showing the data being used in SimplyAnalytics, a map and a ranking (click to enlarge):

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