Measuring the correlations between the individual items and constructs they load with is?
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A construct is an underlying latent trait that cannot be directly observed and measured\n\nAn item bank is a comprehensive catalog of items for use in creating psychometrically sound fixed-length, brief form and/or adaptive tests. These items should span the various construct dimensions and function along their respective continua at various difficulty levels. "The idea is that the test user can select test items as required to make up a particular test" (Choppin, 1978). The flexibility provided by an item bank allows the researcher to utilize valid, reliable and well-validated items without being required to re-calibrate those items each time they are used. Items selected for future use can differ, thus allowing optimized use of individual items.
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It would depend on what you are trying to measure. But if your confused about the difference between which is the independent variable and which is the dependent variable, think of this experiment: If you were correlating the outdoor temperature with the months of the year, the "months" would be the independent variable and the "temperature" would be the dependent variable. If you were graphing this data, you would put the dependent variable's data on the y-axis (vertical) and the indenpendent varable's data on the x-axis (horizontal.)
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I would use spearman's rho.  The only difference between spearman's rho and pearson's correlation coefficient is that the spearman's rho is computed after ranking the numbers in sort order.
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For experimental data it is 100 * (average of the set - measured member of the set) / (average of the set).\n\nIf you are measuring something, how have you established an "Expected Value"? Why are you conducting an experiment/measurement if you already know what to expect?
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The most common correlation (there are several) is described at:\n\nhttp://mathworld.wolfram.com/CorrelationCoefficient.html\n\nThis is sometimes known as the "product moment correlation".  Some others are: Spearman's rank order correlation and Kendall's tau.
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Not as such.  You don't tell us which correlation coefficient you have in mind to use. However, Works  can help to take the slog out of the calculations.  I know Works is not as powerful as Excel but with a bit of care  it should be possible to create the formulae for analysis of your data.  If you go on to study this sort of thing in depth I would recommend the SPSS software.
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If you are talking about z scores for one particular variable, then you should use a histogram.  Excel does not do histograms, so you should probably use MINITAB or some other statistics program.\n\nThe previous answer only applies if there are two variables involved.
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Here is a graph of .9 which is close to what you have: http://upload.wikimedia.org/wikipedia/en...\nThe way to read this is the diagonal is all r=1 and the rest of the relations are in the opposite positions. The .9 is in the first column... and the second box down. \n\n\nHere is a general graph that explains the relationships:\nhttp://hosting.soonet.ca/eliris/remotese...\n\nJust remember that just because it has a correlation of r=0 doesn't mean there isn't some correlation: http://www.uwsp.edu/psych/stat/7/i30.gif... ...\n\nThese points are obviously related and we can come up with a model to predict the next one.
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Check out the Analytic Hierarchy Process.  It allows you to score your priorities based on categories you designate.  It's a very scientific way to make multivariate decisions.  Sometimes it helps to score priorities in groups to offset subjectivity.
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You must use this formula to find the Coefficient of Correlation for the above equation and the data set that it represents:\n\nr = (X-Xm)(Y-Ym)/(n-1)((Sy)(Sx)\n\nAround 1900, Karl Pearson, developed a technique that describes the relationship between two sets of interval-scaled variables.  This mathematical technique measures the relationship between linear variables.  If all of the data points lie on straight line, then the correlation coefficient is +1.00 or -1.00, depending on the slope of the line of the data set.  And of course, if there is no linear relationship, then the correlation will be zero.\nI like to plot the data set using MS Excel, and then do a linear regression using the built in data tools.  Remember, that you need a data set, X and Y values, to plot a graph of data points.\nHope this helps.
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Broad question! Focusing it would help us answer better.\n\nReally basic stats would do things like tally and sum, average, find the median of the subjects you are testing.\n\nOnce you have those descriptive statistics, you can do things like compare outcomes between groups. Suppose you have two samples of people. One group has a mean life expectancy X. The other has mean life expectancy Y. To determine whether X and Y are significantly different from each other, you could do a Student t-test to determine whether the difference in values of X and Y are truly significant, or differ only by chance.\n\nthat's just one example. there are tons more!
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having 10 or more in three different statistical categories, for example, points, assists and rebounds
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Here are some statistics:
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From checking statistics not so well.....
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The CORREL() function in many major spreadsheet packages, such as Microsoft Excel and Gnumeric calculates Pearson's correlation coefficient.
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not sure if you can but here's what i'd do.  on separate paper, list Erickson's overall developmental theory(ies) and on another paper, list the ecological systems theory.  by using the cross-matching process, and by the standards you set or were set for you, see if you can match 5 or more out of, say, 10. if you can do that, you're on a (buttered) roll.  if not, you're toast!
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# A statistical measure used in computing the correlation coefficient between two variables; the covariance is the mean of (x- x(bar))(yy(bar)) over all pairs of values for the variables x and y, where x(bar) is the mean of the x values and y(bar) is the mean of the y values. A statistical value measuring the simultaneous deviations of x and y variables from their means.\nhttp://helios.bto.ed.ac.uk/bto/glossary/c2.htm\n\n# A measure of the relation between two variables. The correlation coefficient is equal to the covariance of x and y divided by the product of the standard deviation of x and the standard deviation of y.\nhttp://www.indiainfoline.com/bisc/accc.html\n\nfor the formula, you can refer to this site - \nhttp://en.wikipedia.org/wiki/Covariance