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Labor Studies

Locating and Using Data

What type of data am I looking at or looking for?

Summary-Level Data: Published data. Use summary-level data if you are looking for a quick statistic such as the unemployment rates or the GDP for various countries.

Micro-Level Data: This is the numerically-coded results of individual responses, such as census questionnaires, public opinion surveys, etc. You can work with the data and run your own statistical analyses on it via programs such as SPSS, SAS or other statistical software. If you are working with the raw data, you need to use the data documentation (codebook) that accompanies the data and write a program or use an extraction program to have the computer "read" the data into a useable format.

Data Documentation, Data Dictionaries, Codebooks: These provide information on the structure, content, and layout of a data file and, if applicable, the questionnaire used for the survey or study. Many codebooks are available with the data file.

Finding and Citing Data

If you haven't already found data, but need to for your project, use our Finding Data & Statistics to help you find and cite data.

The sources below allow you to browse and search for statistics by topic and will often point you to the appropriate agency and/or publication.

Interpreting Data

Data, like other information you use in your paper, is evidence to support your thesis. Therefore, you must evaluate data just as you would any other supporting information, such as a scholarly article, webpage, or document. Think of the 6 question words (who, what, where, when, why, and how) when deciding whether to use the data as evidence in your paper. For example, who produced the data? Are they trustworthy? Was their method of obtaining or creating the data credible?

If you are using summary-level data (a chart or graph from a book, website, or scholarly article) you MUST analyze it carefully before you use it. There is a lot of bad data visualization out there and, if you interpret it incorrectly and then use it to bolster your argument, the rest of your writing becomes suspect as well.

If you are using the micro-level or raw data for your analysis, this is less of an issue and you, as the creator of the data visualization need to make sure you keep these ideas in mind.

Reading the data:

  1. Determine how the figure/table is set up.
    • What are units on the axes (for a figure) or heading of the columns (for a table)? Make sure you understand what these units mean.
    • Pay attention to the symbols on a figure. For example, the differences between dotted and solid lines.
    • If there is a data dictionary or codebook, it is helpful to review it.
    • If it is generated by a database, be aware that some use algorithms to determine data points. These can be proprietary and secret, so you may not know exactly how the data is being calculated.
  2. Now look at the pattern in the data.
    • For a figure with lines, what is their pattern? Do they increase, decrease, increase or decrease and then then level off? In a table do the numbers increase or decrease across the column?
  3. Examining tables, graphs, and charts carefully should give you a good idea of the question addressed by the data and the methodology (e.g., experiment, survey, etc.) done to get the data.

Interpreting the data:

  1. What conclusions can you draw from the pattern that you have described?
  2. What do these results tell you about what is being studied?
  3. How do they fit into the larger picture of the discipline?

Note: Interpretations can differ from person to person. This is part of why interpreting data is tricky.

Text adapted from Ecoplexity, "Interpreting Figures and Tables." Portland State University (2010).

EXAMPLE : When a chart is not what it seems to be.

The chart on the left, below, was used in 2015 by Republican Representative Jason Chaffetz of Utah, during the U.S. House Committee Hearing on Planned Parenthood. Look closely at the two lines, they cross each other but are widely different figures.

The chart on the left is the original chart. The chart on the right is with the data graphed more accurately. Even then, the numbers are so different from the top line to the bottom line that they appear skewed even in the graph on the right. This is bad data visualization.

Planned Parenthood House Committee Hearing Graph with bad axis graph and correct. The Cancer Screening & Prevention Services starts at 2,007,371 in 2006 and is 935,573 in 2013, Abortions is 289,750 in 2006 and 327,000 in 2013 but the original graph has the lines crossing each other as if they are comparable numbers.

Image on the left from:

More Examples