Charles Spearman: Theory of Intelligence & Overview Video

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  • 1:11 Bringing Science to…
  • 2:08 Statistical Analysis &…
  • 5:54 What Made Spearman…
  • 7:04 The G Factor & the Human Body
  • 7:43 Spearman's Legacy
  • 8:18 Lesson Summary
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Lesson Transcript
Instructor: Nancy Zingrone

Nancy has a PhD in psychology and has taught introductory courses in psychology as as well as published original research on personality and individual differences.

The field of psychology has always been interested in intelligence, what it is, and how it impacts an individual's ability to be successful in life. In this lesson, you will meet Charles Spearman and learn about his general factor theory of intelligence and some of his other accomplishments.

Charles Spearman's Road to Psychology

Charles Spearman, who lived from 1863 to 1945, was an English psychologist who came to experimental psychology a bit late in life. He studied engineering in college and worked as an engineer in Burma in the 1880s. In his early thirties, Spearman decided to leave the military to become an experimental psychologist.

Spearman headed to Leipzig in Germany to study with the famous experimental psychologist, Wilhelm Wundt, who ran the only PhD program in psychology then in existence. University College, London, hired Spearman as an instructor and researcher in 1907. He worked there for 25 years, rising through the ranks and becoming a very prominent professor of psychology before he retired in 1932. His work influenced a number of important psychologists who came after him.

Charles Spearman
Charles Spearman

Known as an early founder of the psychological testing movement, Spearman refined the methodology of intelligence testing, including the statistical techniques then used to analyze test results. His greatest theoretical contribution was proposing the theory of the g factor, in which he argued that general intelligence formed the bedrock from which all other mental abilities developed.

Bringing Science to the Study of Intelligence

Intelligence is defined as the ability to obtain and use knowledge in a productive way. For thousands of years before Spearman, intellectuals and philosophers worked to understand the concept. Although Spearman loved philosophy, like many others of his generation, he wanted to do more than just talk. To accomplish this, he brought the scientific method to intelligence testing.

For Spearman, understanding why some people grew up to be great thinkers and learners and why other people struggled to grasp even the simplest thing had both practical and philosophical implications. By observing and measuring intelligence in a systematic way, Spearman believed, researchers would be able to define all the variables that influenced the differing levels and types of intellectual ability people have. More importantly, research would discover how these variables fit together.

Statistical Analysis and Tests of Mental Variables

When Spearman became involved, the psychological testing movement was already underway. Many tests had been developed that looked at different types of intellectual variables, such as math learning or reasoning skills. When psychologists attempted to correlate the results of these tests to one another, they didn't have much luck. So while Spearman believed that there was an underlying general factor of intelligence that should predict what level of specific mental abilities individuals would show on tests, his colleagues were not finding those relationships in their data. Spearman began to wonder if there was something wrong with the methods his colleagues were using to determine whether relationships existed or not.

At the time, a test called the Pearson Product Moment Correlation was one of the main statistical techniques in use. The Pearson Correlation was named for its inventor, another British academic, Karl Pearson. He based his technique on the concept of correlation first proposed by the great British thinker, Francis Galton.

Galton was very interested in heredity, kinship, and the differences between people on a variety of characteristics. His theory was that individual specific characteristics are related to each other, and this 'co-relation' would be apparent in the way in which certain characteristics either appeared with others or changed with others.

Imagine that you are interested in the correlation of health and intelligence. If so, you might ask, 'Does evidence of intellectual ability increase as overall health increases?' Or, if you were interested in the relationships of reason to emotion, you might ask, 'Are less emotional people more logical?' By asking these types of questions, you signal that you believe these aspects of human ability and/or behavior are linked and might vary together in some lawful way.

The Pearson Correlation test was a step forward in the analysis of correlation. It was designed to show whether one aspect of human ability was related to another by mathematically plotting the results from one test against the results of another test. Before Pearson's test was available, researchers checked the amount to which one set of results was correlated to another set by using other types of less sensitive statistical analyses or by manually plotting individual scores from the two tests on a graph like the ones shown here.

Graph of correlations

To make this a little clearer, if the results from Test A plotted along the x-axis of a graph clustered closely to results from Test B, plotted along the y-axis as the first graph shows, a researcher could say that there was a strong correlation between the two sets of results. Another way of looking at that relationship is to say that when there is a strong correlation between two tests, you would expect that almost everybody who had a high score on Test A also had a high score on Test B, and vice versa.

If the results from Test A were weakly correlated to the results from Test B, the researcher would see a graph like the middle image. With that kind of graph, you might find that more of your participants who got a high score on Test A also got a high score on Test B, but not that many more. Finally, if Test A results and Test B results had no correlation to one another whatsoever, plotting them on the graph would yield something like the third image, in which the test results are all over the place and don't cluster together at all. In that case, you wouldn't be able to make any prediction about what the scores would be on Test B just by looking at the scores on Test A.

The genius of Pearson's Correlation test was that it allowed researchers to list the two sets of results and then use a statistical formula to determine the level of correlation between the results without having to sit down and plot each individual test result on a graph.

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