Topic: Understanding How to Read and Use (or misuse!) Data
Statistical Concepts Covered: In this applet, you’ll learn that making observations and collecting data is only the start of the scientific process. The ways that you look at the data, and the values that you use in your comparisons, and even the way you set up your graphs can have a large influence in the way you interpret your results.
Introduction:
In this first chapter you’ve learned how the field of social psychology has evolved as a scientific field of study. Is the field of psychology growing, or is it an area of study that is in decline? In order to answer that question – and many of the other questions you’ll see throughout this text – you need to read the data.
This applet will allow you to explore data that has been collected as a part of the National Science Foundation’s and National Center for Science and Engineering Statistics’ (NCSES) Survey of Earned Doctorates. This data shows the number of PhD degrees that have been awarded in different disciplines each year since 1982. Given the reliable source of this data, you should be able to come to a reliable conclusion, right? It turns out that this question, like many others, does not necessarily have a clear-cut answer. As you investigate the historical PhD data for psychology and other fields, you should begin to see that even with accurate numeric data, interpreting results can be heavily influenced by what portion of the data you look at, what your comparison groups are, and how the data is displayed.
1) Approximately how many psychology PhDs were awarded in 2012? (Select “Psychology” as the field to display.)
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2) Which of the following disciplines has experienced the greatest amount of growth in the past 30 years? (Choose a field or group to display from the drop-down. Cycle through each one to see which have experienced the greatest amount of growth.)
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3) Based on the graph showing the number of psychology PhDs awarded since 1982, what is the best way to characterize the overall trend in the field? (Select “Psychology (min/max)” as the field to display.)
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4) If you plot a graph that shows the total number of PhDs awarded across all fields, you should see that the overall number of doctorates awarded is increasing. If you plot the total number of PhDs alongside the number of psychology PhDs, how does your perception of the trend for psychology PhDs change? (Select “Psychology vs. All” as the field to display.)
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5) What changed in the graphs that you used to answer question 3 and question 4 that lead to such different answers?
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6) If the total number of PhDs awarded is increasing faster than the number of psychology PhDs. awarded, then what must be true about the percentage of all degrees awarded that are psychology degrees? (If you are unsure, you can use the applet to plot the percentage of psychology degrees in order to visualize the relationship by selecting “% Psychology Overall” as the field to display.)
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7) Compare the number of PhDs awarded in psychology to the number awarded in computer science, which is considered to be a very trendy and attractive field of study. Which of the following statements best describes the trends from 1982 through 2012? (Select “Psychology vs. Computer and information sciences” as the field to display.)
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8) If you wanted to convince someone that psychology, as a field, was showing considerable growth, which of the following other data sets would you want to display on the same plot to make your argument as convincing as possible? (Cycle through the various fields and note how these fields compare to the psychology field.)
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9) If instead of using the data from 1982 onward, you focus only on the computer science and psychology degrees from 2006 – 2012, how does that change the relationship between the two fields? (Select “Psychology vs. Computer and information sciences” as the field to display.)
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10) The data available here spans 30 years and can show relatively stable long-term trends. However, as illustrated in question 9, looking at a smaller segment of the data can often change your interpretation of the results. If someone wanted to distort the data for personal or political reasons, they could carefully pick a small set of data points to make the trend appear very different from the actual data. If someone wanted to suggest that mechanical engineering, as a field, was in decline, which data points could they selectively plot? (Select “Psychology vs. Mechanical Engineering” as the field to display.)
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