January 8, 1999

alpha: the probability of making a Type I error (rejecting the null hypothesis when it is true) anti-compensatory program: experimental participants outscore the control participants on pretreatment measures for which higher scores mean "better" ARIMA model: autoregressive, integrated, moving average model for the analysis of time-series data assignment variable: variable correlated with the outcome and used to assign persons to treatment groups; the source of non-randomized selection effects autocorrelation: in a time series, the degree of correlation between two time points separated by a fixed length of time or lag autocorrelogram: a graph of the strength of autocorrelation as a function of lag length autoregressive model: for time-series and longitudinal data, the current value is assumed to be a function of the previous values plus a random component autoregressive coefficient: the degree to which the current value is influenced by the previous value blocking: matching using a categorical variable; term often used in randomized experiments change score analysis: the use of change as the outcome in over-time analyses Cohen's d: mean difference between two treatment groups divided by the pooled within groups standard deviation; a measure of effect size compensatory program: control participants outscore the experimental participants on pretreatment measures for which higher scores mean "better" construct validity: the measure actually taps the intended theoretical construct control group: persons who have not received the treatment to whom the treated group is compared correlation: the degree of linear association between two standardized variables; the slope divided by the perfect slope; ranges from minus one to plus one; measure of effect size covariate: a measure that is correlated with the outcome but not affected by the treatment or the outcome cross-lagged panel correlation (CLPC): a method for ruling out the plausible rival hypothesis of spuriousness using longitudinal data cycle: in a time series, observations separated by a constant interval tend to be similar to one another effect size: the magnitude of the standardized effect of a treatment variable on an outcome error variance: in psychometrics, the variance in a measure not due to true variance which is estimated by the measure's variance times one minus the measure's reliability; alternatively in modeling, the unexplained variance in a variable external validity: the generalizability of the results from a study; a threat is the interaction of treatment with another variable fishing: multiple significance tests of essentially the same hypothesis with no adjustment in alpha Galton squeeze diagram: a pair-link diagram in which the levels of one variable are connected to the means of the other variable guesstimate: through a visual examination, not mathematical computation, a statistic (e.g., a mean or slope) is approximated history: the plausible rival hypothesis that change is due to some intervening event and not the treatment horizontal squeeze plot: a scatter plot in which means or guesstimates are computed for each value on the variable on the vertical axis; the scatter plot is squeezed horizontally instrumentation: the plausible rival hypothesis that a change in an outcome is due to a change in the calibration of the measuring device internal validity: valid causal inference; estimating the effect due to a treatment; threatened by plausible rival hypotheses such as regression toward the mean interrupted time-series design: a time series in which the initial observations serve as control and after an intervention is introduced the remaining observations are experimental lag: the time interval between measurement latent variable: a theoretical construct that is imperfectly measured by one or more indicator linearity: the assumption that the relationship between two variables can be best fitted by a straight line Lord's paradox: when treatment groups differ on a pretreatment measure, covarying out that measure and change score analysis yield different conclusions matching: measuring the treatment effect across equivalent scores on a third variable to reduce, but not likely eliminate, bias due to selection maturation: the plausible rival hypothesis that change is due to natural growth and not the intervention measurement error: the random, unsystematic component in a measurement mega-covariate: a covariate that is formed by combining the values of two or more covariates mortality: the plausible rival hypothesis that persons who leave the experimental and control groups do so for different reasons; a type of selection effect multilevel modeling: a statistical method for the analysis of data at two or more levels, e.g., children and classrooms or persons and times multiple regression: a statistical technique for the simultaneous estimation of the effects of several predictors that add together multitrait-multimethod matrix (MTMM): a correlation matrix between a set of variables (i.e., traits) all measured by the same set of methods nonequivalent control group design: treatment and control groups are non-randomly formed and persons are pre- and posttested null hypothesis: the hypothesis that some population value (e.g., a mean difference, a correlation, a regression coefficient) equals some particular value (usually zero) omitted variable: variable not controlled in the statistical that causes the outcome and the assignment variable; the assignment variable; the source of selection effects overadjustment: the estimated treatment is biased in the direction of the difference on the covariate (the covariate being scaled to correlate positively with the outcome) over-fitted regression line: if X is used to predict Y, the predicted values of Y for each value of X, connected by lines pair-link diagram: graph of two-variable association; two vertical lines, one for each variable, and the scores represented by a line connecting these two vertical lines parallel test: a second measure of the same construct that has the same amount of true and error variance and sometimes is assumed to have the same mean perfect-correlation: the slope if there were a perfect correlation; a slope of the standard deviation of the criterion divided by the standard deviation of the predictor plausible rival hypothesis: a threat to internal validity; an alternative explanation of the treatment effect power: the probability of rejecting the null hypothesis; one minus the probability of making a Type II error pre-post design: a group of persons is measured before and after receiving a treatment pretest: a prior measure of the outcome proximal autocorrelation: the correlation between shorter time lags is larger than the correlation between longer lags quasi-simplex: a simplex correlational structure that is attenuated by measurement error random assignment: the assignment of persons into treatment groups by a random rule; persons have a fixed probability of being assigned to a treatment group random selection: the selection of persons into the study randomly from some specified population randomized experiments: studies in which units are randomly assigned to treatments regression discontinuity design: persons are assigned to treatment groups on the basis of a measured variable regression line: if X is used to predict Y, the line that minimizes the sum of squared errors of prediction regression toward the mean: because of a less than perfect correlation, the predicted score of a variable is not as extreme in terms of standard score units than the predictor variable in standard score units reliability: the proportion of variance in a measure that is true, commonly estimated by an internal consistency measure scatter plot: a graph in which the axes are two variables and the points represent the scores of individuals on the variables selection: the plausible rival hypothesis that the treatment difference is due to a pre-existing difference on some unknown variable; that unknown variable is called the assignment variable selection by maturation: persons at the different levels of the assignment variable are changing at different rates selection by regression: persons at the different levels of the assignment variable are regressing to different means shrinkage: the variance of predicted scores using the standard regression prediction formula must be less than or equal to the variance of the observed scores; how much less depends on the correlation between the prediction and the score being predicted simplex: the correlational structure that results from a first-order autoregressive model; the resulting structure is proximally autocorrelated spuriousness: the covariation between two variables is not due to one causing the other, but rather due to the variables both being caused by a third variable standardization: the transformation of a variable so that its mean is zero and its variance is one; Z scoring standardized change score analysis: the variance of the components of a change score have equal variance through standardization; the formula for this analysis is Y - (s_{Y}/s_{X})X stationarity: parameters do not change over time; e.g., the mean and the standard deviations of the pretest and the posttest are the same statistical equating: using multiple regression in an attempt to control presumed selection variables structural equation modeling: models with a causal structure between latent variables synchronous correlation: the correlation between two variables measured at the same time testing: the plausible rival hypothesis that the process of being measured affects subsequent measurements time-reversed analysis: the analysis of data switching the flow of time and determining if the results change time series: data from a single unit that is temporally ordered trait-state-error model: a model of change with three components: a trait or unchanging variable, a state or autoregressive component, and an error or random component treatment: an experimental intervention or program; the variable that contrasts the two groups in an evaluation trend: a constant change in a variable over time true-score estimate: given an observed score, the predicted true score is regressed or shrunk toward the mean using the formula: M_{X}+ r_{X}(X - M_{X}) where r_{X}is the measure's reliability true variance: the portion of variance in a measure that is not error; estimated by the measure's variance times its reliability Type I error: rejecting the null hypothesis when it is true; probability denoted as alpha Type II error: not rejecting the null hypothesis when it is false; its probability denoted as beta and power equals one minus beta underadjustment: the estimated treatment is biased in the direction opposite from the difference on the covariate (the covariate scaled to correlate positively with the outcome) vertical squeeze plot: a scatter plot in which means or guesstimates are computed for each value of the variable on the horizontal axis; the scatter plot is squeezed vertically zero-correlation line: a flat line which intersects the mean of the variable being predictedGo back to the previous page.