INDEPENDENT VARIABLE

Sociologyindex, Sociology Books 2008

Causal research examines the world in terms of variables (those things which reveal variation within a population).

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.

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.

There may be more than one dependent variable, because manipulating the independent can influence many different things. For example, an experiment to test the effects of a certain 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.

Variables
Scientists use an experiment to search for cause and effect relationships in nature. In other words, they design an experiment so that changes to one item cause something else to vary in a predictable way.

These changing quantities are called variables. A variable is any factor, trait, or condition that can exist in differing amounts or types. An experiment usually has three kinds of variables: independent, dependent, and controlled.

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.

For example, if you open a faucet (the independent variable), the quantity of water flowing (dependent variable) changes in response--you observe that the water flow increases. The number of dependent variables in an experiment varies, but there is often more than one.

Experiments also have controlled variables. Controlled variables are quantities that a scientist wants to remain constant, and he must observe them as carefully as the dependent variables. For example, if we want to measure how much water flow increases when we open a faucet, it is important to make sure that the water pressure (the controlled variable) is held constant. That's because both the water pressure and the opening of a faucet have an impact on how much water flows. If we change both of them at the same time, we can't be sure how much of the change in water flow is because of the faucet opening and how much because of the water pressure. In other words, it would not be a fair test. Most experiments have more than one controlled variable. Some people refer to controlled variables as "constant variables."

Regionality as an Independent Variable
Interlopers as Agents of Linguistic Change
by Jack Chambers
Dialectologists have always been aware that mobility is a potent force in leveling regional language variants, and for that reason, traditional dialect studies stipulated that their subjects be locals. The criterion of local nativity has deliberately been abandoned in my Dialect Topography project (Chambers 2000). As a survey of urban as well as rural areas, we seek a representative sample of the population, and that includes not only men and women of all classes and ages but also, obviously, residents of the survey area who are relative newcomers to it. Concomitantly, it was necessary to devise a metric for distinguishing indigenes, those subjects born and raised in the survey region, from interlopers, those who arrived there as adults, as well as the various degrees in between (subjects born outside but raised in the survey area, and so on). The metric is known as the Regionality Index (RI), and it is based on where the subject was born, where he or she was raised from 8 to 18, and where the parents were born. I have described the RI calculations in detail elsewhere (Chambers 2000: 10-13; Chambers and Heisler 1999: 40-46), and for my purposes here need only say that each subject receives an index score from 1 to 7, where RI 1 is a true indigene (as defined above) and RI 7 a true interloper, and the in-between scores indicate relative grades of nativeness. Conceptually, the easiest way to interpret the scores is in terms of major thresholds, as follows: - §3 of "Dynamics of dialect convergence." Investigating Change and Variation through Dialect Contact,, ed. Lesley Milroy. Special issue of Sociolinguistics 6 (2002): 117-130.

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. The conclusion is that not only the NYSE, but the entire American securities market, is structured in a way that virtually automatically diverts rents to outsiders. Institutional theory and economic sociology reveal that market structure alone ensures rent transfers from retail investors to market professionals, regardless of the motivations of behavior of the latter. The theory is explained and additional uses suggested. - papers.ssrn.com/sol3/papers.cfm?abstract_id=478401

The Health Services Establishment is Becoming an Independent Variable: A Life of its Own 
Odin W. Anderson, University of Wisconsin-Madison 
Until recently, the health services establishment was assumed to be a product of the social, economic, and scientific medical developments since the turn of the century. It consumed a modest and relatively constant percentage of the gross national product (GNP). It was a dependent variable. 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 grew faster than the GNP and the Consumer Price Index. 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 and empirically how and why this transformation took place. - mcr.sagepub.com/cgi/content/abstract/52/1/6

Regression Analysis of Censored Data with Applications in Perimetry 
Anna Lindgren, Centre for Mathematical Sciences, Mathematical Statistics, Lund University,
Abstract: This 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. Using the independence between the true values and the censoring limits the quantile function for the true values can be rewritten as another quantile function of the observed, censored values, where the quantile value itself is a function of the censoring distribution. The quantile value is estimated non-parametrically and the properties of the resulting quantile function estimate studied by simulations. We also apply this technique in practice to the problem of finding limits for the normal variability in stable glaucomatous visual fields. 
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 value of the dependent variable and the estimated distribution of the independent variable conditional on the fact that it is censored. The distributions are estimated in three different ways, parametrically, assuming, e.g. a two-dimensional normal distribution, semi-parametrically, assuming a normal distribution for the dependent variable given the independent one while estimating the distribution of the independent variable non-parametrically, and non-parametrically estimating the distribution of the independent variable locally in a band around the value of the dependent variable. - maths.lth.se