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- W2038789805 abstract "The desirability of randomized experiments (see Gilbert, Light, & Mosteller, 1975) and the usual need for quasi-experimental designs (because real programs rarely can be administered randomly) are staples of the evaluation literature. Campbell (1969) and others long have advocated the need for acquisition and analysis of longitudinal data in such evaluations. Today's graduate students dutifully study trend data from the famous Connecticut crackdown on speeding and the British Breathalyser experiment to learn how graphs can be interpreted to shed light on possible program effects. Inevitably, the data portrayed represent zero-order effects. An outcome is plotted over time, or several graphs portray multipletime series for the treatment and control groups. Techniques for quantifying changes in these graphs at the time of an intervention (e.g., see Box & Tiao, 1968) were developed early in the evolution of quasiexperimental methods. This paper presents a new technique in which covariates are controlled and eval-" @default.
- W2038789805 created "2016-06-24" @default.
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- W2038789805 date "1983-03-01" @default.
- W2038789805 modified "2023-09-25" @default.
- W2038789805 title "Advanced Ocularmetrics: Graphing Multiple Time Series Residual Data" @default.
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- W2038789805 doi "https://doi.org/10.3102/01623737005001097" @default.
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