Causality examines the world in terms of variables, independent variable and dependent variable. An independent variable is typically the cause, while a dependent variable is the effect. The independent variable is that variable assumed to be the causal variable. In experimental research it is the variable the investigator manipulates. The effect (the dependent variable) is dependent on the causal variable or independent variable. If unemployment is thought to cause crime rates to increase, unemployment is the independent variable (it can vary between high and low) and crime rates the dependent variable. Something which is an independent variable at one time can be a dependent variable at another.

The independent variable, also known as the manipulated variable, lies at the heart of any qualitative experimental design. This is the factor manipulated by the researcher, and it produces one or more results, known as dependent variables. There are often not more than one independent variable tested in an experiment, otherwise it is difficult to determine the influence of each upon the final results. An experiment to test the effects of a fertilizer, upon plant growth, could measure height, number of fruits and the average weight of the fruit produced. All of these are valid analyzable factors, arising from the manipulation of one independent variable, the amount of fertilizer.

The independent variable is the one that is changed by the scientist. To insure a fair test, a good experiment has only one independent variable. As the scientist changes the independent variable, he or she observes what happens. The scientist focuses his or her observations on the dependent variable to see how it responds to the change made to the independent variable. The new value of the dependent variable is caused by and depends on the value of the independent variable.

**Regionality as an
Independent Variable - Interlopers as Agents of Linguistic Change** - Jack Chambers

Dialectologists have always been aware that mobility is a force
in leveling regional language variants, and for that reason, dialect studies stipulated
that their subjects be locals. The criterion of local nativity has deliberately been
abandoned in my Dialectics Topography project (Chambers
2000).

**Structure as an
Independent Variable in Assessing Stock Market Failures **

LAWRENCE E. MITCHELL, George Washington University - Law School

Abstract: The recent frontrunning by specialists on the New York Stock Exchange call for
an explanation of why an institution thought to be efficient has flaws that permit this
activity. Not only the NYSE, but the entire American securities market, is structured in a
way that automatically diverts rents to outsiders. Economic
sociology reveals that market structure alone ensures rent transfers from retail
investors to market professionals, regardless of the motivations of behavior
of the latter.

**The Health Services Establishment is Becoming an Independent Variable: A Life of
its Own** - Odin W. Anderson, University of Wisconsin-Madison

Since the 1950s, sparked by labor-management negotiations for health insurance coverage
and Medicare and Medicaid, plus the dazzling high medical technology such as organ
transplants, the health services establishment took off. It competes with other priorties
for goods and services. It became an independent variable having an impact on society. This essay attempts to demonstrate conceptually with empirical evidence why this transformation took place.

**Regression Analysis of Censored Data with
Applications in Perimetry **

Anna Lindgren, Centre for Mathematical Sciences, Mathematical Statistics, Lund University,

Abstract: Thesis treats regression analysis when either the dependent or the independent
variable is censored. We deal with quantile regression when the dependent variable is
censored. The quantile value is estimated non-parametrically and the properties of the
resulting quantile function estimate studied by simulations.

When the independent variable is censored it is possible to achieve estimates by throwing
away the censored data and estimate the mean function by ordinary least squares using only
the non-censored data. We try to improve on these estimates by redistribution the censored
values to positions based on the values of the dependent variable
and the estimated distribution of the independent variable conditional on the fact that it
is censored.