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Potential Threats to External and Internal Validity

Potential Threats to External and Internal Validity

Potential Threats to External and Internal Validity

Potential Threats to External and Internal Validity

I’m studying and need help with a Psychology question to help me learn.

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Recently, debate has intensified over the “problem” of selective samples in psychological research. Scientists have coined the term WEIRD to describe the vast majority of participants in psychological studies:






What are some potential threats to the internal and external validity of a study that employs WEIRD subjects? What kinds of studies are at particular risk? How might we fix these problems systematically, in our research endeavors?

There are clear differences between WEIRD sample subjects and the rest of the world. From how they view themselves, the world, religion, and their moral reasoning/compass.

External validity means the findings of a study can be generalized to other populations and settings. If samples stick to WEIRD this is in jeopardy because a rich person’s lifestyle in the US is entirely different from a poor middle-eastern. I would hypothesize that the study comparing the two would have drastically different results.

Trying to prove theories that can apply global from results that come from small specific groups could be counterintuitive.

We can try to fix this by gathering researchers to take off the limitations and go after more representative study participants.

Are behavioral studies at risk?

Explanation & Answer: 150 words

Internal validity is the extent to which you can be confident that a cause-and-effect relationship established in a study cannot be explained by other factors.

In other words, can you reasonably draw a causal link between your treatment and the response in an experiment?
Table of contents
Why internal validity matters
Internal validity makes the conclusions of a causal relationship credible and trustworthy. Without high internal validity, an experiment cannot demonstrate a causal link between two variables.

Research example
You want to test the hypothesis that drinking a cup of coffee improves memory. You schedule an equal number of college-aged participants for morning and evening sessions at the laboratory. For convenience, you assign all morning session participants to the treatment group and all evening session participants to the control group.
Once they arrive at the laboratory, the treatment group participants are given a cup of coffee to drink, while control group participants are given water. You also give both groups memory tests. After analyzing the results, you find that the treatment group performed better than the control group on the memory test.

Can you conclude that drinking a cup of coffee improves memory performance?

For your conclusion to be valid, you need to be able to rule out other explanations for the results.

How to check whether your study has internal validity
There are three necessary conditions for internal validity. All three conditions must occur to experimentally establish causality between an independent variable A (your treatment variable) and dependent variable B (your response variable).

Your treatment and response variables change together.
Your treatment precedes changes in your response variables
No confounding or extraneous factors can explain the results of your study.
In the research example above, only two out of the three conditions have been met.

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Drinking coffee and memory performance increased together.
Drinking coffee happened before the memory test.
The time of day of the sessions is an extraneous factor that can equally explain the results of the study.
Because you assigned participants to groups based on the schedule, the groups were different at the start of the study. Any differences in memory performance may be due to a difference in the time of day. Therefore, you cannot say for certain whether the time of day or drinking a cup of coffee improved memory performance.

That means your study has low internal validity, and you cannot deduce a causal relationship between drinking coffee and memory performance.

Trade-off between internal and external validity
External validity is the extent to which you can generalize the findings of a study to other measures, settings or groups. In other words, can you apply the findings of your study to a broader context?

There is an inherent trade-off between internal and external validity; the more you control extraneous factors in your study, the less you can generalize your findings to a broader context.

Research example
In your study of coffee and memory, the external validity depends on the selection of the memory test, the participant inclusion criteria, and the laboratory setting. For example, restricting your participants to college-aged people enhances internal validity at the expense of external validity – the findings of the study may only be generalizable to college-aged populations.
Threats to internal validity and how to counter them
Threats to internal validity are important to recognize and counter in a research design for a robust study. Different threats can apply to single-group and multi-group studies.

Single-group studies
Research example (single-group)
A research team wants to study whether having indoor plants on office desks boosts the productivity of IT employees from a company. The researchers give each of the participating IT employees a plant to place by their desktop for the month-long study. All participants complete a timed productivity task before (pre-test) and after the study (post-test).
Threat Meaning Example
History An unrelated event influences the outcomes. A week before the end of the study, all employees are told that there will be layoffs. The participants are stressed on the date of the post-test, and performance may suffer.
Maturation The outcomes of the study vary as a natural result of time. Most participants are new to the job at the time of the pre-test. A month later, their productivity has improved as a result of time spent working in the position.
Instrumentation Different measures are used in pre-test and post-test phases. In the pre-test, productivity was measured for 15 minutes, while the post-test was over 30 minutes long.
Testing The pre-test influences the outcomes of the post-test. Participants showed higher productivity at the end of the study because the same test was administered. Due to familiarity, or awareness of the study’s purpose, many participants achieved high results.
How to counter threats in single-group studies
Altering the experimental design can counter several threats to internal validity in single-group studies.

Adding a comparable control group counters all threats to single-group studies. If comparable control and treatment groups each face the same threats, the outcomes of the study won’t be affected by them.
A large sample size counters testing, because results would be more sensitive to any variability in the outcomes.
Using filler-tasks or questionnaires to hide the purpose of study also counters testing threats.
Multi-group studies
Research example (multi-group)
A researcher wants to compare whether a phone-based app or traditional flashcards are better for learning vocabulary for the SAT. They divide 11th graders from one school into three groups based on baseline (pre-test) scores on vocabulary. For 15 minutes a day, Group A uses the phone-based app, Group B uses flashcards, while Group C spends the time reading as a control. Three months later, post-test measures of vocabulary are taken.
Threat Meaning Example
Selection bias Groups are not comparable at the beginning of the study. Low-scorers were placed in Group A, while high-scorers were placed in Group B. Because there are already systematic differences between the groups at the baseline, any improvements in group scores may be due to reasons other than the treatment.
Regression to the mean There is a statistical tendency for people who score extremely low or high on a test to score closer to the middle the next time. Because participants are placed into groups based on their initial scores, it’s hard to say whether the outcomes would be due to the treatment or statistical norms.
Social interaction Participants from different groups may compare notes and either figure out the aim of the study or feel resentful of others. Groups B and C may resent Group A because of the access to a phone during class. As such, they could be demoralized and perform poorly.
Attrition Dropout from participants 20% of participants provided unusable data. Almost all of them were from Group C. As a result, it’s hard to compare the two treatment groups to a control group.
How to counter threats in multi-group studies
Altering the experimental design can counter several threats to internal validity in multi-group studies.

Random assignment of participants to groups counters selection bias and regression to the mean by making groups comparable at the start of the study.
Blinding participants to the aim of the study counters the effects of social interaction.