1. Give Me Simple Outputs: Using Excel to Tell Your Story
Wendy Smith
Portland Housing Bureau- City of Portland
Katie Dineen
Joint Office of Homeless Services - Multnomah
Sarah Glover
Transition Projects, Inc

2. Goals:
You will be empowered with ideas on how to use excel to examine your data set, to find patterns, and present your information in easy to digest charts and graphs.
No need for fancy analysis tools.

3. *What this presentation will NOT cover:
This is NOT an excel class, you are expected to have a basic understanding of excel.
We want to give credit where credit is due – much of this session comes out of an NHSDC Data Institute.

4. Data
We have access to lots of it
What story does it tell
Can you make any assumptions
What do you see in the data points of this picture?

5. Moving right along… Data Literacy builds an Insights Driven
Culture (aka: data driven decisions)
You need to:
Prepare the foundation
Read (and comprehend) your data
Work with the data (create actionable insight)
Analyze, dig deeper
Become a data skeptic – dig in to find the answers
Become data fluent – be able to communicate with it

6. Prepare the foundation
Data quality
What data do you need? What questions are you trying to answer?
Codebook + analysis plan
Number of rows
Cleaning>analyzing
Tools and tricks

7. Understand the parameters
This Photo by Unknown Author is licensed under CC BY-NC-SA

8. Why isn’t our data perfect?
Systems
Methods
Formats
Software
Workflows
Errors/workarounds
Upgrades/changes

9. Examples of Data Problems
Too many rows/tabs
Rows are entry/exits, but I want clients
One day entry/exits
Problems with data entry workflows
Unexited clients
Same day entry/exits
Preference for coding no/yes as 0/1
Didn’t include all fields needed
Missing household IDs for singles
Problems with dates
Data that has to be regrouped to be digestible
Source:

10. Data Cleaning Data cleaning = Preparing your data for analysis
Checking data for errors
Addressing missing values
Recoding existing variables
Creating new variables
Removing duplicates
Data cleaning = Preparing your data for
analysis

11. Sample Data Set Variable Meaning OrganizationID
Uniquely identifies the organization
ProjectID
Uniquely identifies the project
ProjectType
The type of project – emergency shelter, transitional housing,
permanent supportive housing
Household type
The type of household – individual or family
DummyDataID
Uniquely identifies the client
EntryDate
Date the client entered the project
ExitDate
Date the client exited the project
Age
Client’s age (calculated)
Race
Client race
Ethnicity
Client ethnicity
Gender
Client gender
(male/female/doesn’t identify as male, female, or transgender)

12. Sample Data Set Variable Meaning VeteranStatus
Whether or not the client is a veteran
(yes/no/data not collected)
DisablingCondition
Whether or not the client has a disability
(yes/no/client refused/data not collected)
CHStatusAtEntry
Whether or not the client is chronically homeless
DateToStreetESSH
The date client started living on the streets, emergency shelter, or Safe Haven (hidden)
LOTHomeless
Number of days homeless (calculated, hidden)
TimesHomeless
PastThreeYears
Number of times client was homeless in past three years (hidden)
MonthsHomeless
Number of months client was homeless in past three years (hidden)
ResidencePrior
Where client lived prior to entering project
Destination
Client destination at exit from project
HouseholdID
Uniquely identifies the client’s household

13. Checking data for errors – How?
Sort data, scroll to see whether everything looks right
Use filters to check whether values appear to be appropriate
Filters allow you to select and view rows in your data that have specific values that you can modify.
Create charts for categorical and quantitative data; examine descriptives for quantitative data to show the distribution
Descriptives include average, count, numerical count, minimum, maximum, and sum. The minimum and maximum can help you see if you might have outliers.

14. Addressing Missing Values
Many ways to deal with
>5% can affect your ability to draw conclusions
Helpful to code it
May be able to create an answer (e.g., exit dates)

15. Recode existing /create new variables
Collapse a large number of values into smaller groupings
Change text values into numeric values
Calculate a new variable based on other fields

16. Removing duplicate values
Check and delete all perfect duplicates
Unit of analysis helps determine what to preserve – in this case, we’ll focus on client-level data
Other options: Consider entry/exits unit of analysis; collapse clients into households
Visual inspection of duplicates using conditional formatting

17. Are you ready? If you haven’t already, you will need
to download the sample data set and the
instructions.
Open “dummy data” in excel and ….
Reminder – time is limited.
Please ask for assistance as needed.

18. It’s your turn Practice!! Open the Data Set
Follow the steps in the handout for Data Cleaning

19. Once you prepare the foundation, understand parameters, clean the data - the results start telling a story.

20. Preliminary Outputs
Pivot Tables
Charts

21. Curiosity Doesn’t Kill the Cat!!

22. It’s your turn Practice!! Open the Data Set
Follow the steps in the handout for Preliminary Outputs

23. Ready to see the final product?
How did it go?
Anyone surprised?
What did you learn?
Any new ideas of where you can take this?

24. Now that we’ve added some color and start to highlight the critical elements the whole picture comes to focus.
Which image do you want to share with your audience?

25. Can you see how the presentation matters, you can emphasize information with colors and intensity

27. Questions????
Thank You!