Data Skills for Media Professionals

A Basic Guide

Ken Blake, Jason Reineke

• Publisher: Wiley-Blackwell
• Year: 2019
• Print ISBN: 9781119118961, 9781119118954
• eBook ISBN: 9781119119067
• Edition: 1
• Topic: Business
• Subtopic: Media & Communications Industry
• Index: Business

Teaches the basic, yet all-important, data skills required by today's media professionals

The authors of Data Skills for Media Professionals have assembled a book that teaches key aspects of data analysis, interactive data visualization and online map-making through an introduction to Google Drive, Google Sheets, and Google My Maps, all free, highly intuitive, platform-agnostic tools available to any reader with a computer and a web connection. Delegating the math and design work to these apps leaves readers free to do the kinds of thinking that media professionals do most often: considering what questions to ask, how to ask them, and how to evaluate and communicate the answers.

Although focused on Google apps, the book draws upon complementary aspects of the free QGIS geographic information system, the free XLMiner Analysis ToolPak Add-on for Google Sheets, and the ubiquitous Microsoft Excel spreadsheet application. Worked examples rely on frequently updated data from the U.S. Bureau of Labor Statistics, the Federal Election Commission, the National Bridge Inventory of structurally deficient bridges, and other federal sources, giving readers the option of immediately applying what they learn to current data they can localize to any area in the United States. The book offers chapters covering: basic data analysis; data visualization; making online maps; Microsoft Excel and pivot tables; matching records with Excel's VLOOKUP function; basic descriptive and inferential statistics; and other functions, tools and techniques. 

• Serves as an excellent supplemental text for easily adding data skills instruction to courses in beginning or advanced writing and reporting
• Features computer screen captures that illustrate each step of each procedure
• Offers downloadable datasets from a companion web page to help students implement the techniques themselves
• Shows realistic examples that illustrate how to perform each technique and how to use it on the job

Data Skills of Media Professionals is an excellent book for students taking skills courses in the more than 100 ACEJMC-accredited journalism and mass communication programs across the United States. It would also greatly benefit those enrolled in advanced or specialized reporting courses, including courses dedicated solely to teaching data skills.

1
Basic Data Analysis

Wrapping up an introduction to data analysis during the first week of a news reporting class recently, one of us asked the two dozen students in attendance whether any of them could name something they had learned from the session. Just when it seemed nobody was going to say anything, a student spoke up. She had learned, she said, that data analysis wasn’t terrifying.

We’re going to demonstrate some basic spreadsheet skills by showing you how to turn a set of federal unemployment rate estimates for nearly 400 metropolitan areas in the United States into a news story that an audience in the Nashville, Tennessee, area, where we live, would want to read. You’ll learn spreadsheet techniques for filtering, computation and sorting. Afterward, you’ll be ready to download the latest data and turn it into a news story that an audience in your area would want to read, assuming your area is in the United States and includes at least one sizable city. In short, you’ll have everything you need to get started right away on the kind of work we presume you picked up this book in the hope of learning how to do. If we had to choose the one thing we most want you to learn from this first chapter, though, it would be the same thing that student learned: Data analysis isn’t terrifying. Nor is it necessarily tedious, confusing or even all that difficult. Instead, it is practical, empowering and, believe it or not, fun.

Some Example Data

Learning data skills requires data, so let’s start with the U.S. Bureau of Labor Statistics’ October 2018 table of preliminary unemployment rates for each of 387 Metropolitan Statistical Areas in the United States (Bureau of Labor Statistics, 2018a). Figure 1.1 shows what the top of the table looked like at the time we were writing this chapter. The bureau updates the table monthly. After you’ve worked through the chapter, we encourage you to download the latest data from the bureau’s Local Area Unemployment Statistics page, www.bls.gov/lau/, and try writing a story suitable for your area. To find the data, scroll down to the “Tables and Maps Created by BLS” area, and click the “Over‐the‐Year Change in Unemployment Rates for Metropolitan Areas” link. A table like the one in Figure 1.1 will appear and will contain the latest MSA‐level unemployment figures.

Figure 1.1 Over‐the‐Year Change in Unemployment Rates for Metropolitan Areas, October 2018. See the link to the data documentation (1). The table shows Metropolitan Statistical Area (2) unemployment data for October 2017 (3), October 2018 (4), and the over‐the year change (5) as well as the rank order of the change (6). The data update monthly.

Source: www.bls.gov/web/metro/laummtch.htm, December 2018.

Always check to make sure you understand how a dataset came to be and what it represents. This one comes from a reasonably reliable source and is accompanied by a link to documentation that clearly explains how and when the data were compiled and what they represent. Specifically, the column labeled “Metropolitan Area” shows the name of each metropolitan area listed. The areas are based on definitions that the Office of Management and Budget updates at least once a decade for federal statistical purposes. Each area name involves at least one city name and one state name that, together, give you an idea of the area’s location and scope. MSAs consist of one or more counties. Some areas in the six New England states are based instead on cities and towns. They’re called New England City and Town Areas, or NECTAs. Of course, the first entry in the column, “United States,” indicates the country as a whole rather than some geographical subdivision.

The column labeled “October 2017 rate” shows the estimated unemployment rate for each area in October of 2017. The “October 2018P rate” column shows the same estimate for October 2018. The “P” indicates that the estimate is still preliminary and may be revised. The bureau bases its estimates on survey data and uses precise definitions to decide who out of the general population qualifies as part of the “labor force,” which members of the labor force will be counted as “employed,” and which members will be counted as “unemployed.” To get the unemployment rate for an area, the bureau divides the number of “unemployed” labor force members by the total labor force, then multiplies the result by 100 to change the figure from a decimal (like .04) to a percentage (like 4 percent, meaning 4 out of every 100).

The “Change” column shows the difference between each October 2017 and October 2018 rate. For example, the Ocean City, NJ MSA’s October 2018 unemployment rate of 5.6 percent represents a decline of 1.9 percentage points compared to the area’s unemployment rate in October 2017, a year earlier. Finally, the “Rank” column shows where each area ranks when sorted, lowest to highest, by the “Change” column. The Ocean City, NJ, MSA ranked first, because its 1.9 percentage point decline in unemployment compared to October 2017 was the largest decline in the country. Note that areas with identical declines have the same rank. For example, the Rocky Mount, NC, and Watertown‐Fort Drum, NY, MSAs both rank third, because both saw an unemployment decline of 1.5 percentage points compared to a year earlier, and that decline was the third‐largest decline in the nation. For more about the data, see “How the Government Measures Unemployment” (Bureau of Labor Statistics, 2015).

An Introductory Tool: Google Sheets

If we’re going to do much more than look at the data, we’re going to need a tool to help us analyze it. Don’t bother reaching for your pocket calculator. We’ll be way out of its league. We need a spreadsheet, a software program that can import, store, organize, display, filter, sort, analyze and graph data – and more. Probably the most widely used spreadsheet, Microsoft Excel, is a favorite of ours. There are more sophisticated programs designed for general statistical analysis that you may have heard of, like R, SPSS, SAS, and STATA. Many general programming languages can be used for data analysis and visualization, too.

Instead of starting with any of those tools, though, we’re going to start with Google Sheets, a Web‐based spreadsheet from Google. We picked it as a starting point for several reasons. Because it’s Web‐based, you never have to install it. You can access it via any web browser on any Internet‐connected computer. Also, you can start a project on one computer, log out, log in on a different computer, and pick up right where you left off. That capability comes in especially handy if you’re a university student who is constantly switching from your own computer to a classroom computer to a computer in a university lab. Furthermore, because Google Sheets runs in a web browser, it will look and handle the same way regardless of whether you are using a Mac or a PC. It can easily produce and publish online, interactive data visualizations, and it’s similar enough to Excel to make switching back and forth between the two fairly easy.

Best of all, it’s free. If you have a Gmail account, you already have access to Google Sheets. If you don’t have a Gmail account, setting one up is a snap.

Google Sheets has drawbacks, too. For example, it tends to become slow and unstable when handling larger datasets, and some of its features are rudimentary compared to their counterparts in Excel and downright primitive compared to those in the other analytical programs we mentioned. It also would be naïve to assume that data saved on servers maintained by a third party are as confidential and secure as data saved on digital storage media more directly under your control. In short, Google Sheets won’t meet all of your data analysis needs. But it’s a good place to start, and it’s similar enough to some of those other programs to make learning them easier.

Getting the Data into a Google Sheet

Let’s open a Google Sheet and get the unemployment data into it. Open the web browser of your choice, and go to http://drive.google.com. If you don’t have a Google account, or if you aren’t logged in to Google on the computer you’re using, your screen will look something like Figure 1.2. If you do not yet h...

Table of contents

• Cover
• Table of Contents
• Preface
• 1 Basic Data Analysis
• 2 Data Visualization
• 3 Making Online Maps
• 4 Microsoft Excel and PivotTables
• 5 Matching Records with Excel's VLOOKUP
• 6 Google Sheets and Inferential Statistics
• 7 Other Functions, Tools and Techniques
• Index
• End User License Agreement