![]() Threats to validity due to nonrandom differences can also arise in between-group randomized experiments. Alternatively, a confidence interval estimates the size of the treatment effect within a range of scores that takes account of uncertainty due to the effects of random differences. A statistical significance test reveals whether the observed outcome difference between the groups is larger than would be expected due to random selection differences (or to other random effects) alone. Statistical inference is the classic means of distinguishing between these two sources of effects. One of the primary threats to validity in a randomized between-participant experiment is “random selection differences.” Because the treatment effect is estimated by comparing the performances of different individuals in the two treatment conditions, observed differences in outcomes could be due to differences in the composition of the two groups (i.e., random selection differences) as well as to the effects of the treatment. Peer pressure caused the treatment group to make far more errors on a subsequent perceptual task than the comparison group. For example, in a series of classic studies of conformity by Solomon Asch in the 1950s, individuals were randomly assigned either to a treatment condition in which peer pressure was exerted or to a comparison condition without peer pressure. Effects of the treatment are estimated based on differences between the groups on the outcome measures. After the different treatments have been administered to the groups, a posttreatment, outcome measure is assessed. In a between-participant randomized experiment, individuals are randomly assigned to treatment conditions. Reichardt, in Encyclopedia of Social Measurement, 2005 Between-Participant Randomized Experiments Approaches to modeling causal treatment effects in light of the noncompliance problem are described using the counterfactual model for causal inference made popular by Rubin (1974, 1977, 1978, 1980 see also Holland 1986). ![]() The causal interpretation of intervention effects may be seriously compromised by confounding factors introduced by subject noncompliance. In the case of educational or behavioral interventions, noncompliance may manifest itself as lowered attendance at intervention sessions or refusal of assigned (or offered) interventions. Essentially, the noncompliant subject self-selects his or her treatment and/or treatment level, thereby disturbing the covariate balance (1995) and its associated safeguard against confounding factors that are afforded by randomized treatment assignment. In randomized experiments designed to evaluate the relative efficacy of two or more treatments or interventions, subject noncompliance with (or nonadherence to) assigned treatment may seriously jeopardize the interpretation of results from such studies. Gitelman, in International Encyclopedia of the Social & Behavioral Sciences, 2001 In addition, noncompliance and attrition may render various treatment conditions noncomparable. It is important to note that randomization may fail to make the experimental conditions equal with respect to various confounders. Pretest measures of the dependent variable are frequently used for this purpose. On the other hand, including such variables in analysis reduces the unexplained within-group variability in the outcome, thus increasing the power for detecting treatment effects. As a result, extraneous variables are expected to be uncorrelated with the manipulated variable or the treatment assignment and their exclusion does not bias the estimates of treatment effects. Random assignment, if successful, makes experimental and control groups probabilistically equal with respect to all potential confounding variables and hence minimizes bias due to initial differences among treatment conditions. (b) All other sources of extraneous variability are controlled by randomization or chance. ![]() These are: (a) the independent or explanatory variable is controlled by direct manipulation. Randomized experiments (Cook and Campbell 1979 see Laboratory Experiment: Methodology Random Assignment: Implementation in Complex Field Settings) offer the most robust method for making causal inferences and imply two powerful means of control. Mehta, in International Encyclopedia of the Social & Behavioral Sciences, 2001 1.1.1 Regression adjustment in randomized experiments
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