asists in context nyacce 2013

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ASISTS in Context

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Venu's presentation at NYACCE in Albany May 2013

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Page 1: Asists in context nyacce 2013

ASISTS in Context

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QR code for presentation

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Data Analysis is like cooking• Steps

– Picking your dish

– Finding the right ingredients

– Cleaning your ingredients

– Preparation

– Using the right tools

– Finishing the dish

– Final touches

– Presenting the final product

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Picking your dish/Picking your question to analyze• Is it an interesting question? Who is asking the

question?

• Do you have the right data to answer these questions? (Tip: it’s not just ASISTS)

• If you don’t have the data, do you know where to get it?

• Do you have the right tools to do the analyses?

• Who is your audience?

• When do you need to be done?

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Sample questions/picking your dish• Should my program focus on ABE or ESL?

• What can my program do to improve retention?

• How can you improve program performance?

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Finding the ingredients (or identifying your data sources)• Questions to ask:

– Does the data have the right information (fields)?

– Do you know what each of the values in the relevant fields stand for?

– Is the time frame relevant to answering the question?

– Is it relevant to the geographical area for which you doing the analysis?

– How reliable is the data?

– Is it one data set or more than one?

– If multiple data sets, can you relate them?

– What are the privacy, legal and security concerns?

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Accuracy/appropriateness of the data/do you have the right spices?• For each data element ask the following questions:– Who collects this data?

– Why is this data being collected?

– Is there a reason for systematic bias in this data?

– Does this field contain a lot of missing data?

– Does this field contain a large number of outlier values?

– Does the data make sense?

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Some data sources to consider/going shopping• ASISTS (https://www.asists.com)

• Census (www.census.gov)

• Immigration data (http://www.dhs.gov/office-immigration-statistics)

• NAAL (http://nces.ed.gov/naal/)

• Other government open data projects– Data.gov (http://www.data.gov/)

– NYC open data portal (https://nycopendata.socrata.com/)

– NYS open data portal (https://data.ny.gov/)

– Data from other government entities (example: School districts)

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To look for in ASISTS• Existing reports (with and without dissagregation)

• Downloads of existing reports

• Data downloads

• Reviewing data screens

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Census data• The Decennial Census

• The American Community Survey (ACS)

• The Current Population Survey (CPS)

• Survey of Income and Program Participation (SIPP)

• Statistics about governments

• Economic census

• The American Fact Finder (AFF)

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The American Fact Finder

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Cleaning your data/washing your vegetables• Watch out for – Outliers and invalid values

– Number of records that make sense

• Simple methods for cleaning your data– Sorting in spreadsheets

– Frequency counts

• Validate against other sources

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A data cleaning exercise• Cleaning up employment status data– Download ASISTS student data

– Do percentages of student with different status

– Compare to employment statistics for your area

– Talk to the manager and intake staff responsible for collecting data

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The tools of the trade• Microsoft Excel

• Access

• For advanced statistics, R, SPSS, SAS

• Census and other data rich web sites

• Google maps

• ARC GIS for mapping

• Other Google tools (Fusion, Ngage)

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Preparing your data• To get your data into the right format for analysis

• Recode

• Sort

• Group

• Deleting unnecessary data

• Removing duplications

• Delete blank rows

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Finishing your dish/analyzing the data• What do you want to say about the data?

• How do you want to say it?

• What analyses are most appropriate to answer your questions? (Tip: you don’t have to be a statistician to do good data analysis)

• How do you want to present your data?

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Presenting your data• Presentation is everything!

• Talking about your result the right way is as important as using the right tables and charts

• Tools– Excel

– PowerPoint

– Google Fusion tables

• Don’t over generalize!

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Data Analysis as

iterative

Asking the Question

Refining the Question

Locating the data

Cleaning data

Analysis

Presentation

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The adult ed data blog• www.adultedgps.blogspot.com– Regular posts of data analyses and policy updates

– Policy and data related tweets

– Searchable

– Downloadable presentations

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Contact Info:

Venu Thelakkat

[email protected]