External Validity
Internal Validity
Construct Validity
Hypothesis Validity
**Wildcard**
Statistical Conclusion Validity, Dichotomizing a Variable, or Bias
100

Define external validity

Are these findings generalizable? (i.e., can they be generalized to another, or external, population, or are they just specific to our population?)

100

Define internal validity

Is there a causal relationship between our independent (x) variable and dependent (y) variable? 

100

Define construct validity

Are we measuring what we say we’re measuring?

100

Define hypothesis validity

•Hypothesis validity: Is our hypothesis ambiguous? Is it falsifiable?

100
Define statistical conclusion validity

•Statistical conclusion validity: Are our statistical methods of analyses and conclusions sound?

•How large was sample size?

•Low sample size = Low statistical power

•Statistical Power: The chance of finding something IF it exists

200
Define/explain what "dichotomizing a variable" means

taking continuous data and making it categorical (i.e., if you have a numerical scale of 0-100, don’t turn it into “high” vs. “low”)

300

Provide an example of a threat to external validity

Example: a research study with an all-female sample size (conclusions cannot be generalized to non-female populations)
300

Provide an example of a threat to internal validity

One example: not having a control group
300

Provide an example of a threat to construct validity

Using a scale of social connectedness to measure extraversion, using a scale of social anxiety to measure introversion

300

Provide an example of a hypothesis with weak hypothesis validity

"The researcher thinks happiness and exercise are related"

300

When evaluating statistical conclusion validity, what sample size do we look for?

n > 100 per group, or 100 overall if no groups

400

Provide an example of ecological validity

A study measuring participants' brain activity in a game of online chess (cannot generalize findings to chess players in general- in-person chess may be different) 

400

Difference between independent and dependent variable

IV = what you're manipulating (predictor variable, x)

DV = what you're measuring (outcome variable, y)

400

Evaluate the hypothesis validity of the below hypothesis:

"Based on previous research findings that individuals living with chronic pain have higher average depression scores than people without chronic pain, Laura hypothesizes that pain and depression will be positively correlated."

Pretty strong (direction of relationship stated, based in theory/previous research)

Weaknesses: chronic pain same as "pain"?

400

What is an example of bias in research

journal bias for positive findings (file drawer effect)

researcher bias to find positive findings to succeed in academia, publish their study, etc.

500

You are a researcher looking to study how sleep impacts undergraduate students' GPAs. You hypothesize that sleep and grades are positively correlated (more sleep --> higher grades). 

How would you make sure you have strong external validity? What would you want to ensure?

- have sample representative of the population you wish to generalize (undergraduate students)

500

What is a confounding variable?

A "third variable" that explains the observed relationship between the IV (x) and DV (y)- a variable that provides an "alternative explanation" to the relationship proposed in a study

500

Why is dichotomizing our data problematic?

We lose a TON of information!

ex: if I’m administering a new medication for doses between 0-100mg and find that the “high group” (51-100mg) is more efficacious than the low group (0-50mg), which dosage do I go with now? We don't know!


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