Content validity - whether or not your instrument reflects the content you are trying to measure.
Convergent validity - measures that should be related are related. Discriminant validity - measures that should not be related are not.
Face Validity - addresses whether or not a measurement instrument is valid on its face.
Predictive validity - the ability to predict something you want to predict.
Correlation - a measure of the association between two variables, closer to 1 means a stronger correlation.
Covariation - a measure of how two variables both vary relative to one another.
Deviation - the difference of a score from the mean.
Error Component - the part of the variance of an observed variable that is due to random measurement errors.
Hypothesis - a theory or prediction made about the relationship between two variables.
Interaction - when the effect of one variable (or factor) is not the same at each level of the other variable (or factor).
Linear Correlation - a statistical measure of the strength of the relationship between variables (e.g., treatment and outcome). The closer the coefficient is to +1 or -1, the stronger the relationship - a positive correlation implies a direct relationship between the variables, a negative correlation implies an inverse relationship.
Linear Regression - the prediction equation which estimates the value of the outcome variable ("y") for any given treatment variable ("x").
Main Effect - the effect of a factor on the dependent variable (response) measured without regard to other factors in the analysis.
Mean - the average of your sample, computed by taking the sum of the individual scores and dividing them by the total number of individuals (sample size, "n").
Median - if you rank the observations according to size, the median is the observation that divides the list into equal halves.
Mode - the observation that occurs most frequently.
Null Hypothesis - the prediction that there is no relationship between your treatment and your outcome.
Random sample - a sample of a population where each member of the population has an equal chance of being in the sample.
Significance level - the probability of finding a relationship between your treatment and effect when there isn't one in reality.
Type I Error - rejecting the null hypothesis when it is true.
Type II Error - accepting the null hypothesis when it is false.
Variation - a measure of the spread of the variable, usually used to describe the deviation from a central value (e.g, the mean). Numerically, its the sum of the squared deviations from the mean.
Copyright 1997, Laura A. Colosi. All rights reserved.
