|
2 X 5 factorial
design |
A factorial design
with one variable having two levels and the other having five levels. |
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|
Alpha (a) |
The probability of a
Type I error. |
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Abscissa |
Horizontal axis. |
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|
Additive law of
probability |
The rule giving the
probability of the occurrence of one or more mutually exclusive events. |
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|
Adjacent values |
Actual data points
that are no more extreme than the inner fences. |
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|
Adjusted
correlation (radj) |
A correction to the
computed correlation coefficient to adjust for the number of predictors
relative to the sample size. |
|
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|
Adjusted means |
Means that have been
adjusted for differences on a covariate. |
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|
All subsets
regression |
The result of a
stepwise multiple regression when the program chooses that set of variables
that has the best correlation with the critierion. |
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|
Alternative
hypothesis (H1) |
The hypothesis that is
adopted when H0 is rejected. Usually the same as the research
hypothesis. |
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|
Analysis of
variance (ANOVA) |
A statistical
technique for testing for differences in the means of several groups. |
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|
Analysis of
covariance |
An analysis of
variance in which the data are adjusted (or controlled) for the presence of
one or more other variables. |
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|
Analytic view |
Definition of probability
in terms of analysis of possible outcomes. |
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|
Array |
The set of Y values associated
with a given X,
or the set of X
values associated with a given Y. |
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|
Asymmetric
relationships |
Log-linear models
where at least one variable is treated as an independent variable and at
least one variable is treated as a dependent variable. |
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|
Backward
elimination |
A stepwise regression
procedure in which we start with all predictors and them eliminate those that
do not contribute significantly or up to some predetermined standard. |
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|
Behrens-Fisher
problem |
An old name given to
the problem of how to compare two independent means when we can not assume
homogeneity of variance. |
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|
Bernoulli trial |
A outcome with one of
two mutually exclusive outcomes--such as pass/fail. |
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|
b (Beta) |
The probability of a
Type II error. |
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Betweensubjects designs |
Designs in which
different subjects serve under the different treatment levels. |
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|
Bimodal |
A distribution having
two distinct peaks. |
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|
Binomial
distribution |
The distribution in
which each of a number of independent trials results in one of two mutually
exclusive outcomes. |
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|
Biserial
correlation |
The correlation
between a continuous variable and a dichotomous variable, where we assume an
underlying normality to the dichotomous variable. Rarely used. |
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|
Bivariate normal
model |
A regression model in
which both X
and Y
are subject to random error. |
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|
Bonferroni
inequality |
An inequality on which
the Bonferrone test is based. It states that the probability of the
occurrence of one or more events can never exceed the sum of their individual
probabilities. |
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|
Bonferroni test |
A multiple comparison
procedure in which the familywise error rate is divided by the number of
comparisons. |
|
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|
Box-and-whisker
plot |
A graphical
representation of the dispersion of a sample. |
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|
Boxplot |
A graphical
representation of the dispersion of a sample. |
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|
Carryover effect |
The effect of previous
trials (conditions) on a subject's performance on subsequent trials. |
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|
Categorical data |
Data representing
counts or number of observations in each category. |
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Cell |
The combination of a
particular row and column‹the set of observations obtained under identical
treatment conditions. |
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|
Censored data |
Data which have been
categorized into two or more groups on the basis of a cutoff score on some
criterion variable. Often a consideration in logistic regression. |
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Centering |
The process of
converting data to deviation scores. |
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Central limit
theorem |
The theorem that
specifies the nature of the sampling distribution of the mean. |
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|
Chi-square
distribution |
The distribution of
the chi-square (c2) statistic. |
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|
Chi-square test |
A statistical test
often used for analyzing categorical data. |
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|
Coefficient of
variation (CV) |
The standard deviation
divided by the mean. |
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|
Collinearity |
The condition in which
the independent variables are (usually highly) correlated with each other. |
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|
Column totals |
The total number of
observations occurring in a column of a contingency table. |
|
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|
Combinations |
The number of ways
objects can be selected without regard to order. |
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|
Combinatorics |
The branch of
mathematics dealing with the number of different ways objects can be selected
or arranged. |
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|
Compound symmetry |
The condition with
constant variances on the main diagonal of a matrix, and constant covariances
off the main diagonal. |
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|
Concordant pairs |
A pair of observations
that are ordered in the same direction on two variables. |
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|
Conditional
distribution |
The distribution of Y for a fixed level of X. |
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Conditional means |
The means for one
variable at individual levels of a second variable. |
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Conditional odds |
The odds of success given some level of another
variable. |
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|
Conditional
probability |
The probability of one
event given
the occurrence of some other event. |
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|
Confidence interval |
An interval, with
limits at either end, with a specified probability of including the parameter
being estimated. |
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|
Confidence limits |
An interval, with
limits at either end, with a specified probability of including the parameter
being estimated. |
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Confounded |
Two variables are said
to be confounded when they are varied simultaneously and their effects cannot
be separated. |
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Constant |
A number that does not
change in value in a given situation. |
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|
Contingency table |
A twodimensional table in which each observation is classified
on the basis of two variables simultaneously. |
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|
Contingency
coefficient |
A coefficient, based
on chi-square, reflecting the degree of relationship exhibited in a
contingency table. |
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|
Continuous
variables |
Variables that take on
any
value. |
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Contrast |
A comparison between
two levels (or two sets of levels) of the independent variable following an
analysis of variance. |
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Cook's D |
A measure of the
influence of an observation in multiple regression. |
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|
Correlation (r) |
Relationship between
variables. |
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|
Correlation
coefficient |
A measure of the
relationship between variables. |
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|
Correlational
measures |
A measure of the
degree of relationship between two variables that are each at least ordinal. |
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Count data |
Data representing
counts or number of observations in each category. |
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Counterbalancing |
An arrangement of
treatment conditions designed to balance out practice effects. |
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|
Covariance (sxy
or covxy) |
A statistic
representing the degree to which two variables vary together. |
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|
Covariance matrix (S) |
A matrix of variances
and covariances among variables. |
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|
Covariate |
A variable whose
influence is controlled in the analysis of covariance. |
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Cramér's phi (Fc) |
The extension of the
phi coefficient to the case of larger contingency tables. |
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Criterion variable |
The variable to be
predicted. |
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Critical value |
The value of a test
statistic at or beyond which we will reject H0 . |
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Cross-correlation |
The correlation
between one predictor and all other predictors. |
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Cross-validation |
The result of taking a
regression equation from one set of data, applying it to a new set of data,
and examining the correlation between the predicted and obtained values on
the new set of data. |
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|
Curvilinear
relationship |
A situation that is
best represented by something other than a straight line. |
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|
Deciles |
Points that divide the
distribution into tenths. |
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|
Decision tree |
Graphical
representation of decisions involved in the choice of statistical procedures. |
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|
Decision making |
A procedure for making
logical decisions on the basis of sample data. |
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|
Degrees of freedom
(df) |
The number of
independent pieces of information remaining after estimating one or more
parameters. |
|
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|
Delta (d) |
A value used in
referring to power tables that combines gamma and the sample size. |
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|
Density |
Height of the curve
for a given value of X- closely related to the probability of an observation
in an interval around X. |
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Dependent variables |
The variable being
measured. The data or score. |
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|
Depth |
Cumulative frequency
counting in from the nearer end. |
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|
Design matrix |
A matrix of coded or
dummy variables representing group membership. |
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|
dferror |
Degrees of freedom
associated with SSerror = k(n - 1). |
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dfgroup |
Degrees of freedom
associated with SSgroup = k - 1. |
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dftotal |
Degrees of freedom
associated with SStotal = N - 1. |
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Deviation scores |
Data in which the mean
has been subtracted from each observation. |
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|
Descriptive
statistics |
Statistics which
describe the sample data without drawing inferences about the larger
population. |
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|
Dichotomous
variables |
Variables that can
take on only two different values. |
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|
Difference scores |
The set of scores
representing the difference between the subjects' performance on two
occasions. Also known as "gain scores." |
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|
Directional test |
A test that rejects
extreme outcomes in only one specified tail of the distribution. |
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|
Discordant pairs |
A pair of observations
that are ordered in opposite directions on two variables. |
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|
Discriminant
analysis |
A procedure for
developing a procedure for optimally discriminating between two groups. This
technique often being replaced with logistic regression. |
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Discrete variables |
Variables that take on
a small set of possible values. |
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Disordinal
interaction |
An interaction in
which group differences reverse their sign at some level of the other
variable. |
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|
Dispersion |
The degree to which
individual data points are distributed around the mean. |
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|
Distance |
The vertical distance
between a point and the regression line. Usually known as the
"residual." |
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|
Distributionfree tests |
Statistical tests that
do not rely on parameter estimation or precise distributional assumptions. |
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|
Dotplot |
A distribution that
represents the frequencies of individual points by stacking dots about the
axis--similar to a histogram. |
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|
Dunn-Sidák test |
A test similar to the
Bonferroni test which is based on a more precise inequality and has slightly
more power. |
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|
Dunnett's test |
A multiple comparison
procedure for comparing each mean against a standard control group mean. |
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|
Effect size (d) |
The difference between
two population means divided by the standard deviation of either population. |
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|
Effective sample
size |
The sample size needed
in equal-sized groups to achieve the power when we have groups of unequal sizes.
It will generally be less than the total number of subjects in the unequal
groups. |
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|
Efficiency |
The degree to which
repeated values for a statistic cluster around the parameter. |
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|
Equally weighted
means |
An analysis of variance
in which cell means all carry the same weight in determining row and column
means, regardless of the number of subjects in each cell. |
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|
Error rate per
comparison (PC) |
The probability of
making a Type I error on any specific comparison when using multiple
comparison procedures. |
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|
Error variance |
The square of the
standard error of estimate. |
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|
Errors of
prediction |
The differences
between Y and Yhat. |
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|
Eta squared (h2) |
A measure of the
magnitude of effect. Also known as the correlation ratio. |
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|
Event |
The outcome of a
trial. |
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Exhaustive |
A set of events that
represents all possible outcomes. |
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Expected value |
The average value
calculated for a statistic over an infinite number of samples. |
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|
Expected
frequencies |
The expected value for
the number of observations in a cell if H0 is true. |
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|
Experimental
hypothesis |
Another name for the
research hypothesis. |
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|
Exploratory data
analysis (EDA) |
A set of techniques
developed by Tukey for presenting data in visually meaningful ways. |
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|
External validity |
The ability to
generalize the results from this experiment to a larger population. |
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|
Factorial design |
An experimental design
in which every level of each variable is paired with every level of each
other variable. |
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|
Factors |
Another word for
independent variables in the analysis of variance. |
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|
Familywise error
rate |
The probability that a
family of comparisons contains at least one Type I error. |
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|
|
Fisher's Least
Significant Difference Test (LSD) |
A multiple comparison
technique that requires a significant overall F, and that involves
standard t
tests between pairs of means. Also known as the "protected t test." |
|
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|
First order
interaction |
The interaction of two
variables. Also known as a "simple interaction." |
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|
Fixed marginal
totals |
The situation in which
the marginal totals in a contingency table are known before the data are
collected and are not subject to sampling error. |
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|
Fixed model Anova |
An analysis of
variance model in which the levels of the independent variable are treated as
fixed. |
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|
Fixed variable |
A variable that takes
on a specific set of values. An independent variable who levels are assigned
by the experimenter. |
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|
Fractiles |
A generic name for
statistics such as deciles, percentiles, and quartiles. |
|
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|
Frequency
distribution |
A distribution in
which the values of the dependent variable are tabled or plotted against
their frequency of occurrence. |
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|
Frequency data |
Data representing
counts or number of observations in each category. |
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|
Friedman's rank
test for k
correlated samples |
A nonparametric test
analogous to a standard one-way repeatedmeasures analysis of
variance. |
|
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|
Gamma |
The symbol for the
effect size. |
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|
|
Gamma function (G) |
A statistical function
closely related to factorials. |
|
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|
|
General linear
model |
The basic model
underlying the analysis of variance and multiple regression. |
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|
Geomteric mean |
A mean of n objects that is
computed by taking the nth root of the product of the n terms. |
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|
Goodnessoffit test |
A test for comparing
observed frequencies with theoretically predicted frequencies. |
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|
Grand total (SX) |
The sum of all of the
observations. |
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|
H-spread |
The range between the
two hinges. |
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|
Harmonic mean |
The number of elements
to be averaged divided by the sum of the reciprocals of the elements. |
|
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|
Heavy tailed
distribution |
A distribution with a
higher percentage of scores in the tails than we would expect in a normal distribution. |
|
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|
Heterogeneity of
variance |
A situation in which
samples are drawn from populations having different variances. |
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|
Heterogeneous
subsamples |
Data in which the
sample of observations could be subdivided into two distinct sets on the
basis of some other variable. |
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|
Hierarchical
log-linear model |
A model in which the
presence of an interaction requires the inclusion of any main effects that
comprise that interaction. |
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|
Hierarchical
(sequential) sums of squares |
Sums of squares in the
analysis of variance where later terms in the model are adjusted only for
terms that precede them. |
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|
higher order
interaction |
The interaction of
three or more variables. |
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|
Hinge location |
The location of the
hinge in an ordered series. |
|
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|
|
Hinges (Quartiles) |
Those points that cut
off the bottom and top quarter of a distribution. |
|
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|
|
Histogram |
Graph in which
rectangles are used to represent frequencies of observations within each
interval. |
|
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|
|
Homogeneity of
regression |
The assumption that
the regression line expressing the dependent variable as a function of a
covariate is constant across several groups or conditions. |
|
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|
|
Homogeneity of
variance |
The situation in which
two or more populations have equal variances. |
|
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|
Homogeneity of
variance in arrays |
The requirement that
the variance in Y associated with one value of X is the same as the
variance in Y
associated with other values of X. |
|
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|
|
Hyperspace |
Multidimensional space
beyond the three dimensions that we can easily represent. |
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|
Hypothesis testing |
A process by which
decisions are made concerning the values of parameters. |
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|
Independent
variables |
Those variables
controlled by the experimenter. |
|
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|
Independent events |
Events are independent
when the occurrence of one has no effect on the probability of the occurrence
of the other. |
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|
Inferential statistics |
That branch of
statistics that involves drawing inferences about parameters of the
population(s) from which you have sampled. |
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|
Influence |
A measure of the
degree to which an individual data point can influence the obtained value of a
regression coefficient. |
|
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|
Inner fences |
Points that are 1.5
times the H-spread above and below the appropriate hinge. |
|
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|
|
Interaction |
A situation in a
factorial design in which the effects of one independent variable depend upon
the level of another independent variable. |
|
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|
Intercept |
The value of Y when X
is 0. |
|
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|
|
Intercorrelation
matrix |
A matrix (table)
showing the pairwise correlations between all variables. |
|
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|
|
Interquartile range |
The range of the
middle 50% of the observations. |
|
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|
|
Internal validity |
The degree to which a
study if logically sound and free of confounding variables. |
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|
Interval scale |
Scale on which equal
intervals between objects represent equal differences‹differences are
meaningful. |
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|
Interval estimate |
A range of values
estimated to include the parameter. |
|
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|
Intraclass
correlation |
A measure of the
degree of relationship between two variables. It is usually squared. |
|
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|
|
Joint probability |
The probability of the
co-occurrence of two or more events. |
|
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|
|
Kappa (k) |
Cohen's measure of
agreement based on a contingency table. |
|
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|
|
Kendall's
coefficient of concordance (W) |
A coefficient of
agreement among two or more judges. |
|
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|
|
Kendall's tau |
A correlation for
ranked data which relies on the number of inversions of the rank order of one
variable when the other variable is ranked in order. |
|
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|
|
KruskalWallis
one-way analysis of variance |
A nonparametric test
analogous to a standard one-way analysis of variance. |
|
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|
|
Kurtosis |
A measure of the
peakedness of a distribution. |
|
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|
|
Latin
square design |
A design which varies
the order of presentation of stimuli in such a way as to distribute sequence
effects across the design. |
|
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|
|
Leading digits
(most significant digits) |
Left-most digits of a
number. |
|
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|
|
Least significant
difference test |
A technique in which
we run t
tests between pairs of means only if the analysis of variance was
significant. |
|
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|
|
Leaves |
Horizontal axis of
display containing the trailing digits. |
|
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|
|
Leptokurtic |
A distribution that
has relatively more scores in the center and in the tails. |
|
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|
|
Leverage |
The degree to which an
observation is unusual with respect to the predictor variables. Similar
to an outlier. |
|
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|
|
Likelihood ratio
chi-square |
An alternative
procedure for calculating the chi-square statistic--most commonly used in
log-linear models |
|
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|
|
Linear combination |
The sum of a weighted
set of means. |
|
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|
|
Linear contrast |
A linear combination
where the sum of the squared weights sum to 0. |
|
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|
|
Linear regression
model |
A regression model in
which the independent variable (X) is not subject to random error. |
|
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|
|
Linear relationship |
A situation in which
the best-fitting regression line is a straight line. |
|
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|
|
Linear
transformation |
A transformation
involving addition, subtraction, multiplication, or division of or by a
constant. |
|
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|
|
Linear regression |
Regression in which
the relationship is linear. |
|
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|
|
Linearity of
regression |
The assumption that
the best fitting line for a bivariate set of data in linear (straight).. |
|
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|
|
Log-linear models |
Models for handling
multiple categorical variables, such as a contingency table with three or
more variables. |
|
|
|
|
Logistic regression |
A variant of standard
regression used when the dependent variable is a dichotomy, such as
success/failure. |
|
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|
|
Logit (transform) |
The natural log of the
odds of success. |
|
|
|
|
Magnitude of effect |
A measure of the
degree to which variability among observations can be attributed to
treatments. |
|
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|
|
Main diagonal |
The diagonal cells of
a matrix from upper left to lower right. |
|
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|
|
Main effect |
The effect of one
independent variable averaged across the levels of the other independent
variable(s). |
|
|
|
|
MannWhitney U test |
A nonparametric test
for comparing the central tendency of two independent samples. |
|
|
|
|
Marginal
distribution |
The distribution of Y across all values of X. In other words, the
distribution of Y ignoring X. |
|
|
|
|
Marginal totals |
Totals for the levels
of one variable summed across the levels of the other variable. |
|
|
|
|
Matched samples |
An experimental design
in which the same subject is observed under more than one treatment. |
|
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|
|
Matched-samples t test |
A t test comparing the
means of matched (or repeated) samples. |
|
|
|
|
Matrix algebra |
Algebra in which you
work with matrices of elements or variables instead of individual elements. |
|
|
|
|
Mean |
The sum of the scores
divided by the number of scores. |
|
|
|
|
Mean absolute
deviation (m.a.d.) |
Mean of the absolute
deviations about the mean. |
|
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|
|
Measurement |
The assignment of
numbers to objects. |
|
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|
|
Measurement data |
Data obtained by
measuring objects or events. |
|
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|
|
Measures of
association |
Measures, often based
on the chi-square statistic, that reflect the degree of relationship between
two variables. The variables are often only nominal. |
|
|
|
|
Measures of central
tendency |
Numerical values
referring to the center of the distribution. |
|
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|
|
Measures of
location |
Another term for
measures of central tendency. |
|
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|
Median (Med) |
The score
corresponding to the point having 50% of the observations below it when
observations are arranged in numerical order. |
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Median location |
The location of the
median in an ordered series. |
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Mesokurtic |
A distribution with a
neutral degree of kurtosis. |
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Midpoints |
Center of interval --
average of upper and lower limits. |
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Mixed model designs |
Anova designs with one
or more between subjects factors and one or more repeated measures factor.
Also refers to designs with both fixed and random independent variables. |
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Modality |
The term used to refer
to the number of major peaks in a distribution. |
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Mode (Mo) |
The most commonly
occurring score. |
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Monotonic
relationship |
A relationship
represented by a regression line that is continually increasing (or
decreasing), but perhaps not in a straight line. |
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MSbetween
groups (MSgroup) |
Variability among
group means. |
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MSwithin (MSerror) |
Variability among
subjects in the same treatment group. |
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Multicategory case |
A situation in which
data can be sorted into more that two categories. |
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Multicollinearity |
A condition in which a
set of predictor variables are highly correlated among themselves. |
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Multinomial
distribution |
The distribution in
which each of a number of independent trials results in one of two or more mutually exclusive
outcomes. |
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Multiple comparison
techniques |
Techniques for making
comparisons between two or more group means subsequent to an analysis of
variance. |
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Multiple
correlation coefficient (R0.123..p) |
The correlation
between one variable (Y) and a set of p predictors. |
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Multiple regression |
Regression with two or
more independent variables. |
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Multiplicative law
of probability |
The rule giving the
probability of the joint occurrence of independent events. |
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Multivariate
analysis of variance (Manova) |
An analysis of
variance with two or more dependent variables. |
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Multivariate
outliers |
Observations that are
outliers in some multivariate space. |
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Multivariate
procedures |
Procedures that deal
with two or more dependent variables simultaneously. |
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Multivariate normal
distribution |
A generalization of
the normal distribution to the joint distribution of two or more variables. |
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Mutually exclusive |
Two events are
mutually exclusive when the occurrence of one precludes the occurrence of the
other. |
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N factorial (!) |
N*(N-1)*(N-2)*(N-3)*...*1 |
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Negative
relationship |
A relationship in
which increases in one variable are associated with decreases in the other. |
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Negatively skewed |
A distribution that
trails off to the left. |
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Newman-Keuls test |
A popular multiple
comparison procedure for making pairwise comparisons among means. |
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Nominal scale |
Numbers used only to
distinguish among objects. |
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Noncentrality
parameter |
The degree to which
the mean of the sampling distribution of the test statistic departs from its
mean when the null hypothesis is true. |
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Nondirectional test |
A test that rejects
extreme outcomes in either tail of the distribution. |
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Non-equivalent
groups design |
A design in which the
experimental groups differ on one or more important variables at the start of
the experiment. |
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Nonparametric tests |
Statistical tests that
do not rely on parameter estimation or precise distributional assumptions. |
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Normal distribution |
A specific
distribution having a characteristic bell-shaped form. |
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Normality |
Usually refers to the
assumption behind most parametric tests that the data are normally
distributed in the population. |
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Normality in arrays |
The assumption that
the Y
values in any array of X are normally distributed. |
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Null hypothesis (H0
) |
The statistical
hypothesis tested by the statistical procedure. Usually a hypothesis of no
difference or no relationship. |
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Observed
frequencies |
The cell frequencies
that were actually observed--as distinguished from expected frequencies. |
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Odds |
The ratio of the
probability (p)
that an event occurs to the |
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Odds ratio (W) |
The ratio of two odds. |
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Off-diagonal
elements |
The elements of a
matrix that are not on the main diagonal. |
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Omega squared (w2) |
A less biased measure
of the magnitude of effect. |
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One-tailed test |
A test that rejects
extreme outcomes in only one specified tail of the distribution. |
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One-way ANOVA |
An analysis of
variance where the groups are defined on only one independent variable. |
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Order effect |
The effect on
performance attributable to the order in which treatments were administered. |
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Ordinal interaction |
An interaction in
which the group differences do not reverse their sign. |
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Ordinal scale |
Numbers used only to
place objects in order. |
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Ordinate |
Vertical axis. |
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Orthogonal
contrasts |
A set of contrasts
that are independent of one another. |
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Outlier |
An extreme point that
stands out from the rest of the distribution. |
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p level |
The probability that a
particular result would occur by chance if H0 is true. The exact
probability of a Type I error. |
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Parameters |
Numerical values
summarizing population data. |
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Parametric tests |
Statistical tests that
involve assumptions about, or estimation of, population parameters. |
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Partial correlation
(r01.2) |
The correlation
between the dependent and independent variables with the effects of one or
more additional independent variables removed from both sides of the
equation. |
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Partialing |
To hold constant the
effect of one variable when looking at the effects of two or more other
variables. |
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Partition |
To divide up a sum of
squares--usually the SStreatment. |
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Pearson product-moment
correlation coefficient (r) |
The most common
correlation coefficient. |
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Pearson's
chi-square (c2) |
The traditional
chi-square statistic--as opposed to the likelihood ratio chi-square. |
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Percentage of
agreement |
The ratio of the
number of times two judges agree, divided by the number of judgments. It is a
measure that does not correct for chance agreement. |
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Percentile |
The point below which
a specified percentage of the observations fall. |
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Permutations |
The number of ways
objects can be arranged taking ordering into account. |
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Phi (F) |
The correlation
coefficient when both of the variables are measured as dichotomies. |
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Platykurtic |
A distribution that is
relatively thick in the "shoulders." |
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Point biserial
correlation (rpb) |
The correlation
coefficient when one of the variables is measured as a dichotomy. |
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Point estimate |
The specific value
taken as the estimate of a parameter. |
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Polynomial trend
coefficient |
A set of coefficients
used for testing for polynomial (e.g., linear, quadratic, ...) trend. |
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Pooled variance |
A weighted average of
the separate sample variances. |
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Population variance |
Variance of the population‹usually
estimated, rarely computed. |
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Population |
Complete set of events
in which you are interested. |
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Positively skewed |
A distribution that
trails off to the right. |
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Power |
The probability of
correctly rejecting a false H0 . |
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Prediction |
The prediction of one
variable (Y)
on the basis of one or more predictor variables (Xi). |
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Predictor variable |
The variable from
which a prediction is made. |
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Proportional
improvement in prediction (PIP) |
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Proportional
reduction in error (PRE) |
The degree to which
the residual error is reduced after taking X into account,
relative to the error without X. |
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Proportionality |
A condition in a
factorial analysis of variance where a certain proportionality exists among
sample sizes. |
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Protected t |
A technique in which
we run t
tests between pairs of means only if the analysis of variance was
significant. Also known as Fisher's LSD test. |
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Quadratic function |
A polynomial function
of the 2nd order, which has one point of inflection. An equation of the form |
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Quantitative data |
Data obtained by
measuring objects or events. |
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Qualitative data |
Non-numerical data,
often in the form of categorical data. |
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Quantiles |
A generic name for
statistics such as deciles, percentiles, and quartiles. |
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Quartiles |
The points which break
the distribution into fourths. |
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Random marginal
totals |
The situation in which
the marginal totals in a contingency table are not known before the data are
collected and are subject to sampling error. |
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Random model Anova |
An analysis of
variance model in which the levels of the independent variable are treated as
a random variable. |
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Random sample |
A sample in which each
member of the population has an equal chance of inclusion. |
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Random variable |
A random variable is a
numerical value which is determined by the outcomes or events of an
experiment.A random independent variable is one who levels vary from one
replication to another, and are not determined by the experimenter. |
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Randomized blocks
design |
A design in which
subjects are matched against one another and put into "blocks" of
subjects of the same size as the number of treatments. Members of each block
are then randomly assigned to treatments. |
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Range |
The distance from the
lowest to the highest score. |
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Range restrictions |
Refers to cases in
which the range over which X or Y varies is artificially limited. |
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Ranked data |
Data for which the
observations have been replaced by their numerical ranks from lowest to
highest. |
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Rankrandomization
tests |
A class of
nonparametric tests based on the theoretical distribution of randomly
assigned ranks. |
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Ratio scale |
A scale with a true
zero point -- ratios are meaningful. |
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Real lower limit |
The points halfway
between the top of one interval and the bottom of the next. |
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Real upper limit |
The points halfway
between the top of one interval and the bottom of the next. |
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Rectangular
distribution |
A distribution in
which all outcomes are equally likely. Also known as a uniform distribution. |
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Reflection |
The process of
reversing the direction of scoring such that high values become low values
and low values become high values. |
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Regression surface |
The equivalent of the
regression line in multidimensional space. |
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Regression |
The prediction of one
variable from knowledge of one or more other variables. |
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Regression equation |
The equation that
predicts Y from X. |
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Regression
coefficients |
The general name given
to the slope and the intercept ‹most often refers just to the slope). |
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Regression line |
The line of best fit
drawn through a scatterplot. |
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Regression surface |
The generalization of
the regression line, or the regression plane, to multidimensional space. |
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Rejection level |
The probability with
which we are willing to reject H0 when it is in fact correct. |
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Rejection region |
The set of outcomes of
an experiment that will lead to rejection of H0 . |
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Related samples |
An experimental design
in which the same subject is observed under more than one treatment. |
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Relative frequency
view |
Definition of
probability in terms of past performance. |
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Repeatedmeasures
designs |
An experimental design
in which each subject receives all levels of at least one independent
variable. |
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Replicate |
The repeat an
experiment. |
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Research hypothesis |
The hypothesis that
the experiment was designed to investigate. |
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Residual |
The difference between
the obtained and predicted values of Y. |
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Residual error |
The error remaining
after the predictor variable(s) has/have been considered. Another term for
residual variance. |
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Residual variance |
The square of the
standard error of estimate. |
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Resistance |
The degree to which an
estimator is not influenced by the presence of outliers.. |
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Rho (r) |
Correlation
coefficient on the population. Also occasionally used for Spearman's
rank-order correlation. |
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Robust |
A test is robust if it
is not seriously disturbed by the violation of underlying assumptions. |
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Row totals |
The total number of
observations occurring in a row of a contingency table. |
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Ryan procedure
(REGWQ) |
A multiple comparison
procedure that holds the familywise error rate at a while having greater
power than Tukey's test. |
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Sample |
Set of actual
observations. Subset of the population. |
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Sample statistics |
Statistics calculated
from a sample and used primarily to describe the sample. |
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Sample variance
(s2) |
Sum of the squared
deviations about the mean divided by N 1. |
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Sample with
replacement |
Sampling in which the
item drawn on trial N is replaced before the drawing on trial N + 1. |
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Sampling
distributions |
The distribution of a
statistic over repeated sampling from a specified population. |
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Sampling
distribution of differences between means |
The distribution of
the differences between means over repeated sampling from the same
population(s). |
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Sampling
distribution of the mean |
The distribution of
sample means over repeated sampling from one population. |
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Sampling error |
Variability of a
statistic from sample to sample due to chance. |
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Sampling fraction |
The fraction of the
number of levels actually used in an experiment to the potential number of
levels that could have been used. In a fixed model the sampling fraction is
1.0, and in a random model it approaches 0.0. |
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Satterthwaite
solution |
See
Welch-Satterthwaite solution |
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Saturated model |
A log-linear model
having as many parameters as unknowns. |
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Scalar algebra |
The plain old kind of
algebra you learned in high school--as opposed to matrix algebra. |
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Scales of
measurement |
Characteristics of
relations among numbers assigned to objects. |
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|
Scatter plot |
A figure in which the
individual data points are plotted in two-dimensional space. |
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Scatter diagram |
A figure in which the
individual data points are plotted in two-dimensional space. |
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Scattergram |
A figure in which the
individual data points are plotted in two-dimensional space. |
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Scheffé test |
A relatively
conservative multiple comparison procedure. |
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Second order
interaction |
The interaction of
three variables. |
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Semi-partial
correlation (r0(1.2)) |
The correlation
between the dependent and the independent variables with the effect of
another variable or variables removed from just the independent variables.
Also known as the part correlation. |
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Sequence effect |
The situation in which
the presentation of one level of the independent variable has an effect on
response to another level of that variable. |
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Sigma (S)--capital |
Symbol indicating
summation. |
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Sigma (s)--lower
case |
Symbol designating the
standard deviation of a population. |
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|
Sign test |
A statistical test
which looks at only the sign, not the magnitude, of the outcomes. (Often used
with the set of difference scores.) |
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Significance level |
The probability with
which we are willing to reject H0 when it is in fact correct. |
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Simple effect |
The effect of one
independent variable at one level of another independent variable. (Also
known as simple main effects.) |
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Simple interaction |
The interaction of two
variables at one level of a third variable. |
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Singular matrix |
A matrix that does not
have a unique inverse. |
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Skewness |
A measure of the
degree to which a distribution is asymmetrical. |
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Slope |
The amount of change
in Y for a one unit change in X. |
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Spearman's
correlation coefficient for ranked data (rs) |
A correlation
coefficient on ranked data. |
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|
Sphericity |
A condition very like
compound symmetry that is required for repeated-measures designs. |
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SScells |
The sum of squares
assessing differences among cell totals. |
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SSerror |
The sum of the squared
residuals. |
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SSerror |
The sum of the sums of
squares within each group. |
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SSgroup |
The sum of squares of
group totals divided by the number of scores per group minus SX2/N. |
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SSsubjects |
The sum of squares of
subject totals. Usually calculated to remove those effects from the error
term. |
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SStotal |
The sum of squares of
all of the scores, regardless of group membership. |
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SSwithin
subjects |
Variability within the
scores from the same subject. |
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SSY |
The sum of the squared
deviations. |
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Standard deviation |
Square root of the
variance. |
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Standard error |
The standard deviation
of a sampling distribution. |
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Standard error of
differences between means |
The standard deviation
of the sampling distribution of the differences between means. |
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Standard error of
estimate |
The average of the
squared deviations about the regression line. |
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Standard scores |
Scores with a predetermined
mean and standard deviation. |
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|
Standard normal
distribution |
A normal distribution
with a mean equal to 0 and variance equal to 1. Denoted N (0, 1). |
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Standardized
regression coefficient (b) |
The regression coefficient
that results from data that have been standardized. |
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Statistics |
Numerical values
summarizing sample data. |
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|
Stem |
Vertical axis of
display containing the leading digits. |
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|
Stem-and-leaf
display |
Graphical display
presenting original data arranged into a histogram. |
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|
Stepwise procedures |
A set of rules for
deriving a regression equation by adding or subtracting one variable at a time from the
regression equation. |
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|
Stepwise regression |
See "stepwise
regression." |
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Stratification |
The partitioning of
subjects into subgroups that are matched on important variables. |
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|
Structural model |
A theoretical model
assumed to underlie the data that expresses the relationship between the
dependent variable and the independent variables. |
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|
Studentized range
statistic (q) |
A test statistic for
testing the difference between the largest and smallest means in a set. |
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|
Studentized
residual |
A statistic for
evaluating the residual for an observation in multiple regression. |
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Student's t distribution |
The sampling
distribution of the t statistic. |
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Subjective
probability |
Definition of probability
in terms of personal subjective belief in the likelihood of an outcome. |
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|
Success/Failure |
An arbitrary
designation of the two possible outcomes in Bernoulli trials. |
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|
Sufficient
statistic |
A statistic that uses
all of the information in a sample. |
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|
Sums of squares |
The sum of the squared
deviations around some point (usually a mean or predicted value). |
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|
Suppressor variable |
A variable whose
correlation with the criterion is opposite in sign from its regression
coefficient. |
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Symmetric |
Having the same shape
on both sides of the center. |
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|
Symmetric
relationships |
Log-linear models in
which all variables are treated as dependent variables. |
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|
T scores |
A set of scores with a
mean of 50 and a standard deviation of 10. |
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|
Tabled distribution
of chi-square |
The table showing the
critical values of chi-square for various degrees of freedom and levels of a when
the null hypothesis is true. |
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|
Test statistics |
The results of a
statistical test. |
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|
Tetrachoric
correlation |
The correlation
between two dichotomous variables when underlying normality of each variable
is assumed. Rarely used. |
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|
Tolerance |
One minus the squared
correlation of a predictor with all other predictors. |
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|
Trailing digits
(least significant digits) |
Right-most digits of a
number. |
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|
Treatment effect |
The difference between
the mean of one treatment (or condition) and the grand mean. |
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|
Trimmed statistics |
Statistics calculated
on trimmed samples. |
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|
Trimmed samples |
Samples with a
percentage of extreme scores removed. |
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|
Tukey's HSD test |
A multiple comparison
procedure for making pairwise comparisons among means while holding the
familywise error rate at a. |
|
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|
|
Two-Tailed test |
A test that rejects
extreme outcomes in either tail of the distribution. |
|
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|
Twoway factorial
design |
An experimental design
involving two independent variables in which every level of one variable is
paired with every level of the other variable. |
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|
Type I error |
The error of rejecting
H0 when it is true. |
|
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|
Type II error |
The error of not
rejecting H0 when it is false. |
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|
|
Unbiased estimator |
A statistic whose
expected value is equal to the parameter to be estimated. |
|
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|
|
Unconditional
probability |
The probability of one
event ignoring
the occurrence or nonoccurrence of some other event. |
|
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|
Unimodal |
A distribution having
one distinct peak. |
|
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|
|
Uniform
distribution |
A distribution in
which all possible outcomes have an equal chance of occurring. Also known as
a rectangular distribution. |
|
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|
Univariate design |
An experimental design
having only one dependent variable. |
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|
Unweighted means |
Row or column means
based on the average of the cell means in that row or column--without giving
greater weight to cells with more observations. Also known as equally
weighted means. |
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|
Validities |
The correlations of
individual predictor variables with the criterion. |
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|
Validity |
The degree to which a
variable measures what it is intended to measure. |
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