Mvs Ch. 1 - Introduction To Multivariate Statistics

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multivariate statistics

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___ provide analysis when there are many independent variables (IVs) and/or many dependent variables (DVs), all correlated with one another to varying degrees.
variables (DVs), all correlated with one another to varying degrees

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single analysis

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If your desisn has many variables, multivariate techniques often let you perform a ___ instead of a series of univariate or bivariate analyses.

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univariate statistics

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The term ___ refers to analyses in which there is a single DV and one more more IVs.

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bivariate statistics

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The term ___ refers to analyses of two variables where neither is an experimental IV and the desire is simply to study the relationship between the variables.

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simultaneously

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With multivariate statistics, you ___ analyze multiple dependent and multiple independent variables.

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inflated error rate

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With multiple DVs, a problem of ___ arises if each DV is tested separately.

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only one DV

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It is dangerous to run an experiment with ___ and risk missing the impact of the IV because the most sensitive DV is not measured.

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continuous variables

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___ are measured on a scale that changes values smoothly rather than in steps.

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discrete variables

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___ take place on a finite and usually small number of values, and there is no smooth transition from one value or category to the next.

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dummy variable coding

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Re-categorization of a discrete variable into a series of dichotomous ones is called ___

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shape of distribution

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The property of variables that is crucial to application of multivariate procedures is not the type of measurement so much as the ___.

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ordinal scale

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The ___ assigns a number to each subject to indicate the subject's position vis-a-vis other subjects along some dimensions.

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rectangular

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A problem with ordinal measures is that their distributions are ___ (one frequency per number) instead of normal, unless tied ranks are permitted and they pile up in the middle.

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samples

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___ are usually measured in order to make generalizations about populations.

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nonexperimental research

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In ___, you investigate relationships among variables in some predefined population.

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experimental research

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In ___, you attempt to create different populations by treating subgroups from an originally homogeneous group differently.

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descriptive statistics

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___ describe samples of subjects in terms of variables or combinations of variables.

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inferential statistics

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___ test hypotheses about differences in populations on the basis of measurements made on samples of subjects.

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orthogonality

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___ is a perfect non-association between variables.

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more than one variable

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A major decision for the multivariate analyst is how to handle the variance that is predictable from ___.

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standard analysis

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In ___, the overlapping variance contributes to the size of the summary statistics of the overall relationship but is not assigned to either variable.

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sequential analysis

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In ___, the researcher assigns priority for entry of variables into equations, and the first one to enter is assigned both unique variance and any overlapping variance it has with other variables.

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reputation for unreliability

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If the multivariate procedures have a ___, it is because solutions change, sometimes dramatically, when different strategies for entry of variables are chosen.

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linear combination

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A ___ is one in which each variable is assigned a weight.

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fewest variables

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A general rule is to get the best solution with the ___.

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overfitting

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With ___, the solution is very good, so good in fact, that it is unlikely to generalize to a population.

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power

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___ represents the probability that effects that actually exist have a chance of producing statistical significance in your eventual data analysis.

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data matrix

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The ___ is an organization of scores in which rows represent subjects and columns represent variables.

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correlation matrix

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In a ___, each row and each column represent a different variable, and the value at the intersection of each row and column is the correlation between the two variables.

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variance-covariance matrix

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A ___ is also square and symmetrical, but the elements in the main diagonal are the variances of each variable, and the off-diagonal elements are covariances between pairs of different variables.

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variance

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___ is the averaged squared deviation of each score from the mean of scores.

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covariance

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___ is the averaged cross-product.

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cross-product

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The ___ is the deviation between one variable and its mean times the deviation between a second variable and its mean

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sum-of-squares and cross-products matrix

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The ___ is a precursor to the variance-covariance matrix in which deviations are not yet averaged.

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residual

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The difference between the predicted and obtained values is known as the ___ and is a measure of error of prediction.