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Research


Although astrology has not been considered a science for some time, it has been the subject of considerable research by astrologers since the beginning of the 20th century. Some of this examination of astrology has taken the form of qualitative research, which has used case study methodologies, as well as data coding, grounded theory, and pattern matching (cycle research) methods. These methods have been largely borrowed from the social sciences and incorporated into the informal traditions of astrological discourse. One area of this type of research examines correspondences between the cycles of long term planetary alignments and historic cultural events as researched for example, by Richard Tarnas. Another area of cycle research, referred to as cosmic cybernetics (a term coined by Theodor Landscheidt) compares the structures and harmonic frequencies of planetary positions with statistical evaluations of data, including economic indexes. This method, which hearkens back to 17th century astrologer/astronomer Johannes Kepler, has been increasingly developed through computer modeling.

By the mid-20th century, a prolonged growth in quantitative research in astrology through the use of statistical inference expanded in an effort to either support or falsify astrological theory and practices. Two examples stand out as the most closely scrutinized and best documented of this type of research. The first is the large scale statistical experiments that challenged astrological theories by the late French psychologist and statistician Michel Gauquelin. The second is the double-blind chart interpretation experiment that challenged astrological practice by American science educator Shawn Carlson. Skepticism, controversy, and accusations have dogged both of these famous studies through unexpected twists and turns.

In 1955, Michel Gauquelin published the claim that although he had failed to find evidence supporting such indicators as the zodiacal signs and aspects in astrology, he had found positive correlations between the diurnal positions of some of the planets and success in professions (such as doctors, scientists, athletes, actors, writers, painters, etc.) that astrology has traditionally associated with those planets. The immense weight of the Gauquelin claim, which in the words of American astronomer George Abell, "would lie well beyond anything that science could at present understand," was grounds for skeptics to maintain, as long as possible, less incredible explanations of those results. The most well known of Gauquelin's results, which have been repeatedly tested in various studies and fought over, was based on the positions of Mars in the natal charts of successful athletes and became known as the "Mars effect".

In a long series of tests and counter-tests spanning decades of discourse, Gauquelin and independent studies by other scientists verified that the Mars effect is not due to astronomical or demographic artifacts, that the methodologies were free from error,  that studies of independently collected data demonstrated the effect,   that birth time shuffle tests supported the presence of the effect, that the Mars effect is not found in ordinary people,  and that the effect cannot be explained by data selection bias.

The issue of selection bias, as to who was a "successful" athlete, had been a major bone of contention. As a method of “raising the hurdle” to objectively eliminate selection bias in the Mars tests, German professor Suitbert Ertel developed a stringent data ranking protocol based on citation frequencies. To everyone’s surprise, when the entire Gauquelin database for athletes, from the famous to the inferior (N = 4,391) was tested, this data-ranking hurdle heightened the astrological effect. Other planetary effects discovered by Gauquelin also crossed this hurdle. However, no assault on these planetary effects has ever successfully crossed the data ranking hurdle to the point where it has been ignored in subsequent skeptical research without giving reasons. Ertel and others have since found the effect present in every data set collected by the skeptical groups. Continuing analysis by using ranking methods suggests that the eminence effects, which at first were thought to be linear correlations, actually have curvilinear shapes.

A second approach to quantitatively testing astrology uses matching tests. The most famous of these is Shawn Carlson's double-blind experiment in which he challenged astrologers to match natal charts to psychological profiles generated by the California Personality Inventory (CPI) test. Following this experiment, Carlson declared a scientific victory against astrology and his study was published in the prestigious science journal Nature in 1985.

Initially, the Carlson experiment was criticized as having an unfairly skewed design, but later deeper flaws in method and analysis emerged. Carlson had disregarded his own stated criteria of evaluation, grouped data into irrelevant categories, rejected unexpected results without reporting them, and drew an illogical conclusion based on the null hypothesis. When the stated measurement criteria was applied and the published data was evaluated according to normal social science, the tests performed by the participating astrologers provided significant evidence (p = .054 with ES = .15, and p = .037 with ES = .10), despite the unfair design, that they were able to successfully match CPIs to natal charts.