Quasi-experimentation: Design & Analysis Issues for Field Settings |
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Page 53
... group is likely to contain as many units whose pretest scores are inflated by error as units whose pretest scores ... experimental group as opposed to another . Selection is therefore pervasive in quasi - experimental research , which is ...
... group is likely to contain as many units whose pretest scores are inflated by error as units whose pretest scores ... experimental group as opposed to another . Selection is therefore pervasive in quasi - experimental research , which is ...
Page 153
... group design , the adjustment can still increase precision , but it usually alters the expected value of the ... experimental group and the one shifted to the left representing the control group scores . If you look along the horizontal ...
... group design , the adjustment can still increase precision , but it usually alters the expected value of the ... experimental group and the one shifted to the left representing the control group scores . If you look along the horizontal ...
Page 159
... experimental condition but would disagree about the size of the effect . In Figure 4.3 ( c ) , the ANOVA and ANCOVA ... group is higher than the mean of the experimental group , suggesting that the treatment effect was negative ( if one ...
... experimental condition but would disagree about the size of the effect . In Figure 4.3 ( c ) , the ANOVA and ANCOVA ... group is higher than the mean of the experimental group , suggesting that the treatment effect was negative ( if one ...
Contents
Causal Inference and the Language of Experimentation | 1 |
The Concept of Cause | 9 |
Implications of the Analysis of Causation | 30 |
Copyright | |
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Common terms and phrases
ACF and PACF alternative analysis ANCOVA ARIMA model attrition autocorrelations of lags bias biased Box-Jenkins breathalyser Campbell causal inference causal relationship cause chapter cohort component construct validity correlation covariate cross-correlation cross-lagged cutting point dependent variable differenced discussion example expected experimental group Figure hypothesis implemented increase individuals interaction internal validity interpretations intervention irrelevant IXXX manipulation matching mean measurement error ment multiple Negative Income Tax nonequivalent group design observations outcome parameters Partial autocorrelations particular pattern persons plausible population possible posttest scores predicted pretest and posttest pretest scores pretest-posttest problem procedure quasi-experiment random assignment randomized experiment regression lines reliability respondents sample SD 2 SD seasonal seasonal lags selection differences Sesame Street settings specified statistical conclusion validity t-statistic theory threats to internal tion transfer function treatment effect estimate treatment group trend variance white noise XXXI XXXXI Y₁ ΧΙ