<span>By strengthened federal government</span>
The rock is weak and molten and can actually flow like lava.
Most national cross-sectional studies indicate that children who spend more time with media are <u>more likely</u> to be overweight than children who do not.
<h3><u>Cross-sectional research: what is it?</u></h3>
A cross-sectional study is a sort of observational research that examines data of variables gathered at one specific point in time across a sample population or a pre-defined subgroup.
Transverse analysis, cross-sectional analysis, and prevalence studies are other names for this sort of research. Even though it doesn't entail running experiments, cross-sectional research is frequently used by academics to understand outcomes in the physical and social sciences as well as many business domains.
<h3><u>Cross-sectional study characteristics.</u></h3>
A cross-sectional study must have, among other things:
- With the same set of data over a predetermined time frame, researchers can conduct a cross-sectional study.
- While similar studies could focus on the same variable of interest, each one looks at a different group of people.
- Contrary to longitudinal investigations, where variables can vary over the course of extended research, the cross-sectional analysis evaluates themes during a single instance with a fixed start and stopping point.
- In cross-sectional studies, one independent variable and one or more dependent variables can both be examined.
Learn more about research with the help of the given link:
brainly.com/question/14391045?referrer=searchResults
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Answer: C. Confounding
Explanation: Confounding variable is a variable or factor which lead us to believe that there is a correlation or relationship between a two variables, However, the observed correlation or relationship is non-existent or spurious. Confounding variables are caused by external influences the outcome of experiments.
In the scenario above, a correlation doesn't exist between stress and employees ability to work, until a confounding variable 'supervisor' came in resulting in a spurious relationship between the previously measured variables.
