Summary of Research methods in psychology by Morling - 3rd edition
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THREATS TO INTERNAL VALIDITY:
There are 12 threats to internal validity. Most of these threats can be prevented with a good experiment design and only occur in the so-called ‘really bad experiment’, also known as the one-group, pre-test/post-test design. The following twelve threats to internal validity exists:
Threat | What happens? | When? | Solution |
Maturation threat | A change in behaviour occurs more or less spontaneously over time. People adapt to changed environments. | One-group, pre-test/post-test design | Using a comparison group |
History threat | A specific event has occurred between the pre-test and the post-test that affects almost every participant systematically (e.g: a change of seasons). | One-group, pre-test/post-test design | Using a comparison group |
Regression threat | If a group’s mean is unusually extreme at the pre-test, it is likely to be less extreme at the post-test, closer to the typical mean (e.g: depressed people have an extreme mean of sadness and this probably will be less extreme when it is tested again). Regression alone does not make an extreme group cross over the mean to the other extreme. | One-group, pre-test/post-test design | Using a comparison group |
Attrition threat | A reduction in participant numbers that occurs when people drop out before the end. This is only a problem if attrition is systematic. | One-group, pre-test/post-test design | Not using the scores of participants that dropped out |
Testing threat | There is a change in the participants as a result of taking a test more than once (e.g: participants might become better at a test if they practice). Participants change over time. | Pre-test/post-test design | Abandoning the pre-test or using a comparison group. |
Instrumentation threat | A measuring instrument changes over time (e.g: observers change the way they observe behaviour over time or two different instruments are used in the pre-test and the post-test that are not equivalent). | Pre-test/post-test design | Abandoning the pre-test, calibrating the instruments and using different groups of participants. |
Selection-history threat | An outside event or factor affects only those at one level of the independent variable. | Pre-test/post-test design | Using comparison groups and checking whether an effect affects both groups |
Selection-attrition threat | Only one of the experimental groups experiences attrition. | Pre-test/post-test design | Using comparison groups and not using the scores of participants that dropped out. |
Observer bias | The researcher’s expectations influence their interpretation of the results. | Any study | Using a double-blind design or a masked design |
Demand characteristics | Participants guess that the study is supposed to be about and change their behaviour in the expected direction. | Any study | Using a double-blind design or a masked design |
Placebo effects | People receive a treatment and believe it will work and thus it has an effect. | Any study | Using a double-blind placebo control study with a control group. |
NULL EFFECTS:
A null effect occurs when the independent variable did not make a difference in the dependent variable. An important possible reason for a null effect can be that the theory is incorrect. A manipulation check is a separate dependent variable to check to see if the manipulation worked. Power is the likelihood that a study will return a statistically significant result, when the independent variable really has an effect. There are several causes for null-effects:
Cause null effect | What happens? | Solution |
Weak manipulation | The independent variable is not operationalized well enough. | Use a manipulation check |
Insensitive measures | There is not an operationalization of the dependent variable that is sensitive enough. | Use a manipulation check |
Ceiling- and floor effects | All participants score either very high or very low. | Use a manipulation check |
Design confounds acting in reverse | A design confound counteracts and this works against any true effect of the independent variable on the dependent variable. | A proper design preventing design confounds |
Noise (error variance / unsystematic variance) | There is too much unsystematic variability within each group. The greater the overlap between the members of two groups, the smaller the effect size. | Keeping the within-group variability to a minimum |
Measurement error | A human or instrument factor inflates or deflates a person’s true score on the dependent variable. | Using reliable and precise tools and measure more instances (e.g: measure more often or measure more people participants) |
Individual differences | There are a lot of individual differences between participants. | Changing the design (e.g: within groups instead of an independent-groups design) or add more participants |
Situation noise | There are external distractions. | Carefully controlling the environment of an experiment |
Studies with that show a null effect are published less often than studies that do show an effect.
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This bundle contains a summary for the book "Research Methods in statistics by B. Morling (third edition)". It contains the following chapters:
- 1, 2, 4, 5, 8, 10, 11, 12, 13, 14.
This bundle contains a summary for the third interim exam of the course "Research Methods & Statistics" given at the University of Amsterdam. It contains the books: "Statistics, the art and science of learning from data by A. Agresti (third edition)" with the chapters:
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