Satisfaction Surveys help you measure your employees (or customer) attitude, opinion and satisfaction levels with your product or work place. Unless you are rich, probably you can not afford survey software tools and need a cheap alternative like excel based employee satisfaction surveys.
Today we will learn how to make a satisfaction survey and consolidate the data using excel.
First make your questionnaire in one excel sheet
See the example above.
Now the fun part, send an email to your colleagues with the questionnaire
And go out, get a cup of coffee and learn excel between sips.
Ok, got the replies? well, move on to next step.
Create a new workbook and copy response sheets to this work book
How? Well, there is a simpler way to do. Open each response sheet and right click on the response tab, select “move or copy” and enable copy option and select the new workbook name.
Copied Everything? Time to Learn 3D References
No, don’t fetch your 3D glasses. 3D references are your way to refer to same cell in multiple sheet. Confused ? See this illustration:
So we will use the 3D formula references to compute average satisfaction level for a question like “how cool your company is?”. Assuming the sheets are arranged such that we have Shelly’s sheet first and Zack’s sheet in the end, and the question satisfaction is entered in cell D5, the formula will look like, =average(Shelly:Zack!D5)
Pretty simple, isn’t it?
That is all, you can use the same principles to create customer satisfaction surveys or other types where you need inputs from several parties in same format.
Of course, if you have internet and Google docs access at work, you can use the Google docs forms to do the same with more time to sip that coffee.
This post is part of our spreadcheats series, learn excel articles in this series and findout how you can be more productive.
One Response to “SQL vs. Power Query – The Ultimate Comparison”
Enjoyed your SQL / Power Query podcast (A LOT). I've used SQL a little longer than Chandoo. Power Query not so much.
Today I still use SQL & VBA for my "go to" applications. While I don't pull billions of rows, I do pull millions. I agree with Chandoo about Power Query (PQ) lack of performance. I've tried to benchmark PQ to SQL and I find that a well written SQL will work much faster. Like mentioned in the podcast, my similar conclusion is that SQL is doing the filtering on the server while PQ is pulling data into the local computer and then filtering the data. I've heard about PQ query folding but I still prefer SQL.
My typical excel application will use SQL to pull data from an Enterprise DB. I load data into Structured Tables and/or Excel Power Pivot (especially if there's lot of data).
I like to have a Control Worksheet to enter parameters, display error messages and have user buttons to execute VBA. I use VBA to build/edit parameters used in the SQL. Sometimes I use parameter-based SQL. Sometimes I create a custom SQL String in a hidden worksheet that I then pull into VBA code (these may build a string of comma separated values that's used with a SQL include). Another SQL trick I like to do is tag my data with a YY-MM, YY-QTR, or YY-Week field constructed form a Transaction Date.
In an application, I like to create a dashboard(s) that may contain hyperlinks that allow the end-user to drill into data. Sometimes the hyperlink will point to worksheet and sometimes to a supporting workbook. In some cases, I use a double click VBA Macro that will pull additional data and direct the user to a supplemental worksheet or pivot table.
In recent years I like Dynamic Formulas & Lambda Functions. I find this preferable to pivot tales and slicers. I like to use a Lambda in conjunction with a cube formula to pull data from a power pivot data model. I.E. a Lambda using a cube formula to aggregate Accounting Data by a general ledger account and financial period. Rather than present info in a power pivot table, you can use this combination to easily build financial reports in a format that's familiar to Accounting Professionals.
One thing that PQ does very well is consolidating data from separate files. In the old days this was always a pain.
I've found that using SQL can be very trying (even for someone with experience). It's largely an iterative process. Start simple then use Xlookup (old days Match/Index). Once you get the relationships correct you can then use SQL joins to construct a well behaved SQL statement.
Most professional enterprise systems offer a schema that's very valuable for constructing SQL statements. For any given enterprise system there's often a community of users that will share SQL. I.E. MS Great Plains was a great source (but I haven't used them in years).
Hope this long reply has value - keep up the good work.