|
Books,
E-Books Great Discounts
| |
CORRELATE, CORRELATION - ZERO ORDER
Sociologyindex, Sociology Books 2011
Any variable which is correlated (the relationship
between the two variables is one of correlation) with another variable. Age and sex are
the two strongest correlates of crime.
CORRELATION
Correlation is a measure of association between two
variables. The variables are not designated as dependent or independent. The two most
popular correlation coefficients are: Spearman's correlation coefficient rho and Pearson's
product-moment correlation coefficient.
When calculating a correlation coefficient for ordinal data, select Spearman's technique.
For interval or ratio-type data, use Pearson's technique.
The value of a correlation coefficient can vary from minus one to plus one. A minus one
indicates a perfect negative correlation, while a plus one indicates a perfect positive
correlation. A correlation of zero means there is no relationship between the two
variables. When there is a negative correlation between two variables, as the value of one
variable increases, the value of the other variable decreases, and vise versa. In other
words, for a negative correlation, the variables work opposite each other. When there is a
positive correlation between two variables, as the value of one variable increases, the
value of the other variable also increases. The variables move together.
The standard error of a correlation coefficient is used to determine the confidence
intervals around a true correlation of zero. If your correlation coefficient falls outside
of this range, then it is significantly different than zero. The standard error can be
calculated for interval or ratio-type data (i.e., only for Pearson's product-moment
correlation).
The significance (probability) of the correlation coefficient is determined from the
t-statistic. The probability of the t-statistic indicates whether the observed correlation
coefficient occurred by chance if the true correlation is zero. In other words, it asks if
the correlation is significantly different than zero. When the t-statistic is calculated
for Spearman's rank-difference correlation coefficient, there must be at least 30 cases
before the t-distribution can be used to determine the probability. If there are fewer
than 30 cases, you must refer to a special table to find the probability of the
correlation coefficient.
Criminologists from an empiricist perspective tend to look at the social world in terms of
variables (anything which varies within a population or group rather than being constant).
Everyone in your class is a student so that is a constant, however, there is a great deal
of variation by factors like sex, age, income, program, GPA, religion, ethnic heritage. If
one gathers information from the whole class on these variables we might begin to see that
some variables vary in patterned ways. People with a particular ethnic heritage may tend
to be more religious than those from other heritages. This would suggest a correlation; as
one variable varies, so does the other. If there were more students of that particular
ethnic heritage in the class then religiosity for the group would also increase. As one
goes up, so does the other. This is referred to as a positive correlation. If one variable
goes up and the other down, this is called a negative relationship. For example, as age
goes up, the crime rate goes down, is a negative (or sometimes called an
‘inverse’) correlation. A correlation does not mean that one variable causes the
other. A causal relationship has to be determined by further research work.
CORRELATION - ZERO ORDER
A correlation between two variables which does not include a control variable. A
first-order correlation, then, would include one control variable as well as the
independent and dependent variables.
What is the meaning of zero or near zero correlation? It
means simply that two things vary separately. That is, when the magnitudes of one thing
are high; the other's magnitudes are sometimes high, and sometimes low. It is through such
uncorrelated variation--such independence of things--that we can sharply discriminate
between phenomena.
I should point out that there are two ways of viewing independent variation. One is that
the more distinct and unrelated the covariation, the greater the independence. Then, a
zero correlation represents complete independence and -1.00 or 1.00 indicates complete
dependence. Independence viewed in this way is called statistical independence. Two
variables are then statistically independent if their correlation is zero.
"Two variables can have a causal relation even in the
absence of a non-zero correlation. Zero-order correlations can be spuriously small as well
as spuriously large. This outcome is especially likely in the complex causal networks that
likely underlie real-world phenomena. Hence, the three conditions for causal inference
from correlational data are misspecified. They probably reduce to two: temporal priority
and a non-zero correlation after controlling for all reasonable third variables."
Alan & Bo's Correlation & Causality Blog - correlation-causality.blogspot.com
"The example I use in class is the equation I've been developing over the years to
predict the greatness assessments of US presidents.* It turns out that one of the best
predictors in a 6-variable multiple regression equation is whether or not a president was
assassinated while in office. Yet assassination does not have a significant zero-order
correlation. How can this be? Well, another major predictor of leader performance is
duration of tenure in office, and this variable quite understandably has a negative
correlation with assassination. On the average, assassinated presidents have shorter
tenures. So the positive association between tenure duration and the global leadership
assessment masks the positive impact of assassination. Only when both are put into the
same equation will the causal impact of assassination emerge. In addition, the predictive
power of tenure duration is increased because its true effect size is no longer obscured
by assassination. In the literature, this is sometimes called "cooperative
suppression" (a term that seems inappropriate in the current example!)."
Alan & Bo's Correlation & Causality Blog - correlation-causality.blogspot.com
Zero-order correlation matrices are used as the starting point in the analysis of
causal structure inherent to the data.
Theory of correlation - Zero-Order, Partial and Multiple Correlation Coefficients;.
Correlation Ratios; Weighted Correlations
| |
Books,
E-Books Great Discounts
|