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

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.

*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.

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?

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

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

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

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

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: https://tinyurl.com/yba9kubk

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

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)

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

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.

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)

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

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

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.

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

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

Preliminary Outputs Pivot Tables Charts

Curiosity Doesn’t Kill the Cat!!

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

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?

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?

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

Questions???? Thank You!