?-level
The probability of making a Type I error
The usual ?-level value
.05
Alternative Hypothesis
The prediction that there will be an effect (i.e. that your experimental manipulation will have some effect or that certain variables will relate to each other.)
Analysis of Variance
A statistical procedure that uses the F-ratio to test the overall fit of a linear model. In experimental research this linear model tends to be defined in terms of group means and the resulting ANOVA is therefore an overall test of whether group means dif
ANOVA
Acronym for Analysis of Variance
Between-group design
another name for independent design.
Between-subject design
another name for independent design.
?i
Standardized regression coefficient. Indicates the strength of relationship between a given predictor, i, and an outcome in a standardized form. It is the change in the outcome (in standard deviations) associated with a one standard deviation change in th
Bimodal
a description of a distribution of observations that has two modes.
?-level
The probablility of making a Type ii error (Cohen suggests a maximum value of 0.2)
Standardization
To overcome the problem of dependence on the measurement scale, we need to convert the covariance into a standard set of units.
Covariance
A crude measure of the 'average' relationship between two variables. Shared variance.
Range of correlation coefficient
has to lie between -1 and +1
coefficient of +1
indicates a perfect positive relationship
coefficient of -1
indicates a perfect negative relationship
coefficient of 0
indicates no linear relationship at all
effect size of +/- .1
small effect size
effect size of +/- .3
medium effect size
effect size of +/1 .5
large effect size
tertium quid
causality between two variables cannot be assumed because there may be other measured or unmeasured variables affects the results==the third variable.
bivariate correlation
correlation between two variables
partial correlation
looks at the relationship between two variables while 'controlling' the effect of one or more additional variables
coefficient of determination
correlation coefficient squared is a measure of the amount of variability in one variable that is shared by the other
correlation coefficient
we can measure the relationship between two variables using correlation coefficients
correlation coefficients lie between what?
-1 and +1
partial correlation
quantifies the relationship between two variables while controlling for the effects of a third variable on both variable in the original correlation
semi-partial correlation
quantifies the relationship between two variables while controlling for the effects of a third variable on only one of the variables in the original correlationl
Regression analysis
a way of predicting an outcome variable from one predictor variable (simple regression) or several predictor variables (multiple regression)
simple regression
a way of predicting and outcome variable from one predictor variable
multiple regression
a way of predicting an outcome variable from several predictor variables
model used in regress
linear model is used
residuals
the vertical differences between the line and the actual data because the line is our model: we use it to predict values of Y from values of the X variable. same as deviation (but called residuals!)
simple regression
a way of predicting values of one variable from another
R�
tells us how much variance is explained by the model compared to how much variance there is to explain in the first place. It is the proportion of variance in the outcome variable that is shared by the predictor variable.
F
tells us how much variability the model can explain relative to how much it can't explain. it's the ratio of how good the model is compared to how bad it is.
b value
tells us the gradient of the regression line and the strength of the relationship between a predictor and the outcome variable. If it is significant (.05) then the predictor variable significantly predicts the outcome variable
grand mean
the mean of all scores
independent t-test
used to test different groups of people, a test using the t-statistic that establishes whether two means collected from independent samples differ significantly
assumptions of the independent t-test
*variances in these populations are roughly equal (homogeneity of variance)
*scores are independent (because they come from different people)
dependent t-test
compares two means, when those means have come from the same entities; for example, if you have used the same participants in each of two experimental conditions
Adjusted R�
a measure of the loss of predictive power or shinkage in regression. The adjusted R� tells us how much variance in the outcome would be accounted for if the model had been derived from te population from which the sample was taken.
content validity
evidence that the content of a test corresponds to the content of the construct it was designed to cover
confounding variable
a variable that we may or may not have measured other than the predictor variables in which we're interested that potentially affects an outcome variable
deviance
the difference between the observed value of a variable and the value of that variable predicted by a statistical model
dichotomous
description of a variable that consists of only two categories, e.g. the variable gender is dichotomous because it consists of only two categories: male and female
effect size
an objective and (usually) standardized measure of the magnitude of an observed effect. Measures include Cohen's d and Pearson's correlations coefficient, r.
predictor variable
a variable that is se to try to predict values of another variable known as an outcome variable
Residual(s)
the difference between the value a model predicts and the value observed in the data on which the model is based. When the residual is calculated for each observation in a data set the resulting collection is referred to as the residuals.
shrinkage
the loss of the predictive power of a regression model if the model had been derived from the population from which the sample was taken, rather than the sample itself
standard deviation
an estimate of the average variability (spread) of a set of data measured in the same units of measurement as the original data. It is the square root of the variance.
variance
an estimate of average variability (spread) of a set of data. It is the sum of squares divided by the number of values on which the sum of squares is based minus 1
�
the mean of a population of scores
?
usually stands for 'error'
df
degrees of freedom
N
denotes the total sample size
n
denotes the sample size of a particular group
r
Pearson's correlation coefficeint
s�
the variance of a sample of data
s
the standard deviation of a sample of data
dependent variable
a variable thought to be affected by changes in an independent variable. You can think of this variable as an 'outcome.'
H?
the null hypothesis
H?
the alternative hypothesis
Alternative hypothesis (example)
Big Brother contestants will score higher on personality disorder questionnaires than members of the public
Null hypothesis (example)
Big Brother contestants and members of teh public wil not differ in their scores on personality disorder questionnaires
variance of a single variable represents. . .
represents the average amount that the data vary from the mean
cross-product deviations
what we get when we multiply the deviations of one variable by the corresponding deviations of a second variable