Which of the following are Quasi experimental designs choose all that apply
PPA 696 RESEARCH METHODSQUASI-EXPERIMENTAL RESEARCH DESIGNSComparison Group DesignThreats to Internal Validity Threats to External Validity Interrupted Time Series Design Interrupted Time Series with Comparison Group Quasi-experimental designs came about because of: 1) difficulty of applying the classical natural science method to the social sciences 2) overemphasis on theory testing and development 3) high cost of classic natural science methods 4) development of new statistical tools that allowed for statistical control There are several types of quasi-experimental designs, including: time series design equivalent time series samples equivalent samples materials design non-equivalent control group counterbalanced designs separate sample pre-test/post-test separate sample pre-test/post-test control group multiple time series design institutional cycle design regression-discontinuity design Comparison Group Pre-test/Post-test DesignIn a quasi-experimental design, the research substitutes statistical "controls" for the absence of physical control of the experimental situation. The most common quasi-experimental design is the Comparison Group Pre-test/Post-test Design. This design is the same as the classic controlled experimental design except that the subjects cannot be randomly assigned to either the experimental or the control group, or the researcher cannot control which group will get the treatment. In other words, participants do not all have the same chance of being in the control or the experimental groups, or of receiving or not receiving the treatment.This design can be diagrammed as follows: Show O1 X O2 This can be illustrated by the following research study to determine whether a home weatherization program for low
income families reduced home energy consumption.
The non-weatherized homes used more BTUs per month (+2.48% more) over the same time period compared to the weatherized homes. The non-weatherized homes also used more BTUs per degree day per square foot; and saved less money (an average of $.001 per degree day). The difference between the change in the experimental group (down 10.95%) and the change in the control group (up 2.48%) is +13.43%. This is the amount of savings that can be attributed to the weatherization program. The quasi-experimental design is not as strong in controlling for threats to the internal and external validity of the study as the true controlled experimental design. Controlling for Threats to Internal Validity1) History: did some other current event effect the change in the dependent variable? No, because both groups experienced the same current events.2) Maturation: were changes in the dependent variable due to normal developmental processes? No, because both groups experienced the same developmental processes. 3) Statistical Regression: did subjects come from low or high performing groups? Both groups were low income families but not necessarily high energy users. 4) Selection: were the subjects self-selected into experimental and control groups, which could affect the dependent variable? No, both groups had applied to the weatherization program, and had similar floor space, number of occupants, and percent owner-occupied. 5) Experimental Mortality: did some subjects drop out? did this affect the results? There were some homes eliminated because of moves, being away from home, or unable to get accurate fuel records. 6) Testing: Did the pre-test affect the scores on the post-test? No, both groups supplied energy records. 7) Instrumentation: Did the measurement method change during the research? No, both groups supplied energy records. 8) Design contamination: did the control group find out about the experimental treatment? did either group
have a reason to want to make the research succeed or fail? None noted. Controlling for Threats to External Validity1) Unique program features: None noted.2) Effects of Selection: None noted. 3) Effects of Setting: Study was done at one location in Minnesota; 4) Effects of History: Study was done during a period of high energy costs; 5) Effects of Testing: None noted. 6) Reactive effects of experimental arrangements: Need to replicate the findings in other locations and other time periods. Quasi-experimental designs may be weak in controlling for threats to internal validity, but can be quite strong in controlling for threats to external validity. It may be difficult to control which police are switched to a four-day ten-hour shift, or which children are given a new
method of learning a foreign language. However, since the research takes place in a natural setting, it may have wide applicability to other similar settings. Interrupted Time Series DesignThis design uses several waves of observation before and after the introduction of the independent (treatment) variable X. It is diagrammed as follows:O1 O2 O3 O4 X O5 O6 O7 O8 This may be illustrated by a study designed to test whether the implementation of a crackdown on speeding in a given state reduces the traffic fatality rate in that state.
The design is not particularly strong at controlling for threats to internal validity: 1) History: did some other current event effect the change in the dependent variable? Researcher must gather qualitative data on possible events that could have affected the fatality rate. 2) Maturation: were changes in the dependent variable due to normal developmental processes? No. 3) Statistical Regression: did subjects come from low or high performing groups? Statistical analysis is used to determine whether changes are due to statistical regression or the independent variable. 4) Selection: were the subjects self-selected into experimental and control groups, which could affect the dependent variable? Researcher must determine whether there were any major changes in the population between the before and after measures. 5) Experimental Mortality: did some subjects drop out? did this affect the results? Researcher must check whether some of the population dropped out after the implementation of the crackdown. 6) Testing: Did the pre-test affect the scores on the post-test? No effects. 7) Instrumentation: Did the measurement method change during the research? Researcher must ensure that fatalities were measured in the same way in all the years considered. 8) Design contamination: did the control group find out about the experimental treatment? did either group have a reason to want to make the research succeed or fail? None noted. Nor is this design strong on controlling for threats to external validity. All the possible threats must be considered, in particular any interaction between the selection of this population and the particular treatment (crackdown) applied. These concerns include a) unique program features; b) effects of selection; c) effects of setting; d) effects of history; e) effects of testing; f) reactive effects of experimental arrangements. Interrupted Time Series Design with Comparison GroupThe addition of a second time series for a comparison group helps to provide a check on some of the threats to validity of the Single Interrupted Time Series Design discussed above, especially history.This design uses several waves of observation in both groups (treatment and comparison groups) before and after the introduction of the independent variable X in the treatment group. It is diagrammed as follows: State A: O1 O2 O3 O4 X O5 O6 O7
O8 This may be illustrated by a study to assess the effect of a crackdown on drunk driving on automobile fatalities in one state, compared to automobile fatalities in another state
without a similar crackdown.
Which of the following are quasiMany types of quasi-experimental designs exist. Here we explain three of the most common types: nonequivalent groups design, regression discontinuity, and natural experiments.
What are the four types of quasiTypes of Quasi-Experimental Design. Non-equivalent group design (NEGD). Regression discontinuity design.. What are examples of quasi experiments?Examples of quasi-experimental studies follow. As one example of a quasi-experimental study, a hospital introduces a new order-entry system and wishes to study the impact of this intervention on the number of medication-related adverse events before and after the intervention.
What are the quasiQuasi-experimental methods are research designs that that aim to identify the impact of a particular intervention, program or event (a "treatment") by comparing treated units (households, groups, villages, schools, firms, etc.) to control units.
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