A Methodological Review of Maharishi Effect Research Assessment of Causality: A Methodological Review When confronted with unorthodox research findings such as those on the Maharishi Effect, there is a tendency to dismiss them as the result of faulty research. Some might say they go to show that statistics can be made to prove anything. In fact, these studies were rigorously conducted. Their publication in leading journals, such as Journal of Conflict Resolution, Social Indicators Research, andJournal of Mind and Behavior indicates that they have met the highest standards for social science research. (Please refer to the summary tables of published articles, presentations, and dissertations for details.) This is particularly true because paradigm-breaking research is always subjected to closer methodological scrutiny than standard research. The review that follows addresses in layman’s terms the basic issues that arise in trying to prove causality in sociological research, and discusses how the research on the Maharishi Effect has addressed these issues. Controlling for Alternative Hypotheses How do we know that the TM and TM-Sidhi program is responsible for observed reductions in crime rate? How do we know that the changes in society are not due to some other influence? In many cases, a number of other variables that could potentially influence the social indicators under study were known from prior research. For example, studies have shown that crime rate is influenced by such factors as the proportion of young adult males in the population, percentage of families with incomes below poverty level, and median years education. In such cases where other relevant variables were known, research on the Maharishi Effect has controlled for these variables by taking their influence into account. Taken as a whole, the 22 studies on the influence of the Maharishi Effect on crime have statistically controlled for the influence of all variables known to influence crime before assessing the effects of the TM and TM-Sidhi program. These studies have specifically controlled for population, college population, population density, geographic region, percent of persons aged 15–29, ratio of police to population, police coverage, neighborhood watch programs, median years of education, family income, per capita income, percentage unemployed, percentage of families with incomes below poverty level, percent in same residence after 5 years, and seasonal effects. Time Series Analysis Many social indicators are influenced by seasonal cycles. Crime rate, for example, decreases in the cold winter months and increases in the hot summer months. Weekends and major holidays also influence many indices of human behavior. Therefore, in studies of the Maharishi Effect, all such seasonal cycles are taken into account before assessing the contribution of the TM and TM-Sidhi program. In addition, there may be upward or downward endogenous trends. Whereas trends represent a systematic change in the level of the process (an overall increase or decline), some data sets may simply drift randomly around some mean level, such as the stock market. Studies on the Maharishi Effect must demonstrate that improvement did not occur at the time of the experiment because of cycles, trends, or drifts. The methodology for taking cycles, trends, and drifts into account is called time series analysis. A time series is a sequence of measurements over equal periods of time, such as days, months, or years. Time series analysis identifies the time-dependent regularities in the data and then calculates a mathematical model that best describes them. Using this mathematical description of the regularities in the data, time series analysis then statistically removes their influence before assessing the possible effects of other variables. The variable that is believed to affect the process is called the exogenous or independent variable. In studies on the Maharishi Effect, the TM and TM-Sidhi program is the independent variable. These studies have examined the influence of change in number of TM and TM-Sidhi participants in the population on various social indicators. Twenty-eight studies on the Maharishi Effect have used time series methodology to show that quality of life improved in a way that could not have been predicted by time-dependent regularities in the data. Furthermore, by removing these regularities from the data, this methodology, in principle, controls for any unknown variable systematically influencing the series. Also, time series analysis allows the researcher to control for other exogenous variables before estimating the impact of the Maharishi Effect. For example, studies of the influence of TM and TM-Sidhi participants on inflation and unemployment in the U.S. used this method to control for monetary growth, change in crude materials prices, industrial productivity, and a measure of the money supply. Time series analysis also provides a precise estimation of the size of the statistical effect. An important issue in statistical modeling is what constitutes the best model of the data. Some experts argue that the best model is the simplest model and that one should only include components that can be easily interpreted, such as weekly or yearly cycles, while other researchers prefer strictly mathematical criteria for determining the best model. Research on the Maharishi Effect has used both criteria to demonstrate that the effect is robust no matter how one defines the “best” model. Causality Controlling for cycles, trends, drifts and other exogenous influences on the data helps to establish that the TM and TM-Sidhi program caused the changes that were measured. Furthermore, time series analysis allows one to directly specify temporal relationships between variables in order to test causal hypotheses. Studies of the Maharishi Effect have used these methods to show that increases in the number of participants in the TM and TM-Sidhi program is followed by improvement on social indicators, providing support for a causal interpretation. In addition to time series analysis, studies of the Maharishi Effect have used other types of causal analysis including Cross-Lagged Analysis methods and the Lisrel Covariance Structural Model. These other types of causal analysis have also strongly indicated that the TM and TM-Sidhi program cause general improvements in society. Even stronger evidence for causality comes from experiments in which groups of TM-Sidhi participants were formed in populations at arbitrary times with respect to the variables being studied. An example is the study in the Middle East in which the group of TM and TM-Sidhi participants was formed in Jerusalem and its effects studied on Israel and Lebanon. In this study, a list of the measures to be used were lodged in advance of the experiment with an independent review board of scientists. Whenever the group size increased, there were improvements on all available social indicators, including decreased armed conflict in nearby Lebanon. This was especially apparent during a two-week experimental period in which incentives were given to maintain an above-threshold group size. It should be noted that scientists, governmental officials, or the news media were informed in advance of virtually all of these social experiments. Also, the data used were from public records, with most provided by government agencies themselves (e.g., crime and auto accident statistics). Perhaps the strongest case for causality in science is made through replication. By repeating the experiment many times with the same results, new findings become established facts. The history of the Maharishi Effect research is a story of continuous replication over larger samples of a wider range of variables. The first of these studies measured crime rate change in 11 cities in the one-year period after these cities reached 1% of their population practicing TM. Later studies extended these findings. For example, one study found decreased crime trends over a six-year period after they became 1% cities, and another study found a decreased crime trend in a random sample of Standard Statistical Metropolitan Areas over an eight-year period. Still other studies extended the research to show improvements in other variables as well, such as decreased suicides and auto accidents. The more recent time series research has studied longer periods of time on even larger populations. For example, group practice of TM-Sidhi participants began in the fall of 1979 at MIU, soon after research showed that as little as the square root of 1% practicing the TM-Sidhi program collectively would also have such an effect. The impact of this coherence-creating group on the quality of life in the United States during the last 20 years has now been well studied using time series analyses. This research has found that increase in the size of the MIU group is responsible for decreasing crime, suicides, auto fatalities, inflation, and unemployment and as well as for improving relations with the Soviet Union. In summary, the Maharishi Effect has been subjected to more rigorous scrutiny than any other large-scale sociological effect. In fact, this research comprises the only truly experimental research in the history of the social sciences on quality of life at the national and international level. Empirical Confirmation of Mechanisms The Maharishi Effect shows that individuals meditating in one place can influence individuals in another place with no direct interaction between the parties. This phenomenon has been further substantiated by three separate neurophysiological studies. The first study was conducted in August 1979. During that time, about 2,500 experts gathered together in Amherst, Massachusetts, for collective practice of the TM and TM-Sidhi program. Half a continent away, in Fairfield, Iowa, researchers found that during the Amherst meditation times, the intersubject EEG brainwave activity between several volunteer subjects in MIU’s lab became significantly more coherent or in phase. These subjects were unaware of the purpose of the tests and had no knowledge of the meditation times of the Amherst group 1,200 miles away. The effect was measured on six consecutive days during the course, but there was no such effect on the same days in the following month after the larger group meditations had ended. A second study specifically measured the influence of increased brain wave coherence of an individual practicing the TM-Sidhi program on a non-meditator in an adjacent room. In each of a series of trials, different pairs of TM-Sidhi participants (Sidhas) and non-meditators were hooked up to the same EEG machine. This arrangement allowed a transfer function analysis of the relationship between the brain wave patterns of the two individuals. In each case, the Sidha’s brain wave coherence led the non-meditator’s coherence by several seconds. That is, when there was an increase in the Sidha’s brain wave coherence, several seconds later there was an increase in the non-meditator’s brain wave coherence. This finding was highly significant statistically. The third set of studies correlated changes in the level of serotonin, a neurotransmitter, in non-meditators living in Fairfield, Iowa, with the number of people collectively practicing the TM-Sidhi program at MIU. Low levels of serotonin in the brain are associated with behavioral problems, such as increased aggression and depression, and high levels are associated with experiences of well-being. Previous research had established that individuals practicing the TM technique have higher levels of serotonin. In this later set of studies, nightly excretion rates of 5-HIAA (the chief metabolite of serotonin) were measured over periods of 50—91 days. These studies found that increased group size of TM-Sidhi practitioners significantly correlated with increased levels of serotonin in non-meditators. Taken together these laboratory studies indicate that individuals acting from the underlying unified field of consciousness during practice of the TM and TM-Sidhi program positively influence the psychophysiological functioning of non-meditators. This psychophysiological influence appears to be the basis of the collective behavioral changes in society produced by the Maharishi Effect. It should be emphasized that in these studies the subjects never interacted, either directly or indirectly. Therefore, the observed effects cannot be explained by classical theories of social interaction. Instead, explanation of these findings requires an understanding of field effects, as described in the next section.