Quasi-Experimental Designs
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| Structures and procedures used in constructing research designs. | show 🗑
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| show | Nonequivalent comparison group design
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| An outcome in which the experimental and the control groups differ at pretesting and both increase from pre- to posttesting, but the experimental group increases at a faster rate. | show 🗑
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| Participates in one group experience a different rate of maturation than participants in another group. | show 🗑
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| show | Selection-history effect
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| show | Selection-instrumentation effect
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| show | Selection-attrition effect
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| show | Selection-regression effect
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| show | First increasing treatment effect
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| show | Second increasing treatment effect
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| show | Crossover effect
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| show | Interrupted time-series design
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| show | Regression discontinuity design
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| Measure used to assign participants to experimental and control groups. Those with scores below the cutoff score are assigned to one group, and those with scores above the cutoff are assigned to the other group. | show 🗑
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| AKA panel study (longitudinal study). ppts are studied over a period of time to see if their risk factors will cause a predicted outcome. Outcome from participants in each cohort is measured and relationships with specific characteristics determined. | show 🗑
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| show | Cross-lagged panel design
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| A research design in which an experimental procedure is applied but all extraneous variables are not controlled. | show 🗑
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| What makes a design a quasi-experimental design? | show 🗑
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| How can you rule out rival hypotheses in quasi-experimental designs? | show 🗑
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| show | 1. control/comparison groups
2. pretest
3. posttest
4. within-participants/bt participants IV
5. inclusion of interesting theoretical IV
6. measurment of theoretical DV
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| Most common quasi-experimental design? | show 🗑
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| Diagram the nonequivalent comparison group design | show 🗑
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| 6 types of Quasi-Experimental Designs/sub categories | show 🗑
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| What are the 6 biases that result from no random assignment in a quasi experimental design? | show 🗑
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| show | 1. Increasing treatment and control groups
2. First increasing treatment effect
3. Second increasing treatment effect
4. Crossover effect
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| What rival hypotheses can exist in Outcome I from using a nonequivalent comparison group design | show 🗑
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| What rival hypotheses can exist in Outcome II from using a nonequivalent comparison group design | show 🗑
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| show | Second increasing threatment efect. (10.5) Control no change but started higher than experimental (s-regression). Experimental increases. Threats: s-regression, selection bias.
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| show | Cross over effect: (10.6) Ideal image. Experiment began lower and ended up higher than control line. Control does not change. Threats: confounding variables.
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| show | 1. Matching: when you cannot randomly select, you can match. But do not match low scores and match high scores because this will cause regression artifact.
2. Statistical control: using stats to determine like-characteristics into groups. ANCOVA.
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| show | Reliabilty adjusted anaylsis of covariance. Another statistically adjusting approach is: propensity score matching and selection modeling.
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| How are rival hypotheses ruled out in a time-series design and what rival hypothesis cannot be ruled out. | show 🗑
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| show | Find out cut off score, either pick the ones above or below cut off score for treatment. Purposely making a regression op= test cut off c=cut off
x=treatment o2=measure
Experimental group: Op C X O2
Control group: Op C O2
contin. line :(
b
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| What is a cohort design and why can this design be used | show 🗑
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| When would you use a cross lagged panel design and what are the three components of this design. Explain the purpose of each component | show 🗑
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