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Chapter 8

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CHAPTER 8 Usually, we cannot manipulate things like: gender; race; age; ethnicity etc.  These are subject variables: Natural treatment: independent variable where exposures to events, situations, or settings that emanate from the ‘real world’ define how participants are selected  Exposure and nonexposure would be the levels of this variable  Researchers can only provide ex post facto (‘after-the-fact’) analysis of the effects on a dependent variable Quasiexperiment: resembles a true experiment except for the degree to which an experimenter can directly control and manipulate one or more of the independent variables.  Whether the experimenter can randomly assign participants to experimental and control conditions (in this case, they can’t) Nonequivalent-Control-Group Designs Nonequivalent-control-group designs: have experimental and comparison groups that are designated before the treatment occurs and are not created by random assignment  Random assignment cannot be used to create groups  Confounds related to equivalency of groups (control vs. experimental) cannot be eliminated o Often high in external validity: o Particularly ecological validity Matching Def.: method of selection of a control group for a quasiexperiment Individual matching: individual cases in the treatment group are matched with similar individuals Aggregate matching: identifying a comparison group that matches the treatment group in the aggregate rather than trying to match individual cases Matching can lead to a problem known as regression artifact: threat to internal validity. This occurs whenever a pretest measure is used for matching.  People who originally had extreme scores on a test will later, after interacting with a group, have scores that are closer to the groups’ scores  regression to the mean: o This can be a confounding factor for interpreting results of studies that match groups based on pretest measures Regression to the Mean (THIS IS EXTRA FROM THE LECTURE) Regression to the mean: a statistical phenomenon that can make natural variation in repeated data look like real change. It happens when unusually large or small measurements tend to be followed by measurements that are closer to the mean. Graphical example of true mean and variation, and of regression to the mean using a Normal distribution. How to Reduce the Effects of Regression the Mean 1. Random allocation to the comparison groups a. If subjects are randomly assigned, the responses from all the groups should be equally affected by RTM. b. The difference between the mean change in the control group and the mean change of the comparison group is then the estimate of the treatment effect after adjusting for RTM 2. Selection of subjects based on multiple characteristics a. The effect of RTM increases with larger measurement variability. To reduce variability, you can select subjects based on two or more baseline measurements b. Study selection criterion (cutoff) is then applied to either the mean of multiple measurements or the second, later measurement (as long as RTM has taken place between the first and later measurements. This can method can be thought of as an attempt to get a better estimate of each subject's true mean before the intervention. Before-and-After Designs Before-and-after designs: have a pretest and posttest but no comparison group.  The participants act as their own control group which means no comparison group Simplest type of B-&-A is the fixed-sample panel design that has one pretest and one posttest. Interrupted-time-series design: examines observations before and after a naturally occurring treatment.  Experimental group(s) where multiple observations have been obtained before and after a naturally occurring treatment. Study John Gibbons and colleagues (2007) Wanted to know whether the policies that discouraged the use of antidepressants in treating children and adolescents had inadvertently led to untreated depression that later led to a jump in suicide rates.  Turns out it was right.  The results had ecological validity as they suggested that antidepressants may help reduce suicide rates of children, adolescents, and adults.  Viewed as quasiexperiment of the effects of a natural treatment (public health warnings) on a dependent variable or outcome measure (suicide rates) Multiple group before-and-after design: several before-and-after comparisons are made involving the same independent and dependent variables but with different groups Study David P. Phillips (1982) Study of the effect of TV soap-opera suicides on the number of actual suicides in the United States  In 12 of 13 comparisons, deaths due to suicide increased from the week before each soap-opera suicide to the week after Repeated-measures panel design: includes several pretest and posttest observations of the same group  Stronger than simple B-&-A designs because they allow the researcher to study the process by which an intervention or treatment has impact over time Time series design: compare the trend in the dependent variable up to the date of the intervention or event whose effect is being studied and the trend in the dependent variable after the intervention  Disparity between the p
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