Causal or Experimental Research Experiments on causal relationships investigate the effect of one or more variables on one or more outcome variables. This type of research also determines if one variable causes another variable to occur or change.
Q. What name is given to a testable prediction about the relationship between two or more events or characteristics?
A hypothesis is a tentative statement about the relationship between two or more variables. It is a specific, testable prediction about what you expect to happen in a study.
Table of Contents
- Q. What name is given to a testable prediction about the relationship between two or more events or characteristics?
- Q. Which is the only design that can determine a cause and effect relationship between two variables?
- Q. Which kind of research uses experiments to identify cause and effect relationships between variables?
- Q. What does a high P-value mean?
- Q. What is a good Pearson r value?
- Q. What does a correlation of .3 mean?
- Q. How do you interpret R squared value?
Q. Which is the only design that can determine a cause and effect relationship between two variables?
A controlled experiment is the only research method that can establish a cause and effect relationship.
Q. Which kind of research uses experiments to identify cause and effect relationships between variables?
Experimental research, often called true experimentation, uses the scientific method to establish the cause-effect relationship among a group of variables that make up a study. The true experiment is often thought of as a laboratory study, but this is not always the case; a laboratory setting has nothing to do with it.
Q. What does a high P-value mean?
A p-value higher than 0.05 (> 0.05) is not statistically significant and indicates strong evidence for the null hypothesis. This means we retain the null hypothesis and reject the alternative hypothesis. You should note that you cannot accept the null hypothesis, we can only reject the null or fail to reject it.
Q. What is a good Pearson r value?
Are there guidelines to interpreting Pearson’s correlation coefficient?
| Coefficient, r | ||
|---|---|---|
| Strength of Association | Positive | Negative |
| Small | .1 to .3 | -0.1 to -0.3 |
| Medium | .3 to .5 | -0.3 to -0.5 |
| Large | .5 to 1.0 | -0.5 to -1.0 |
Q. What does a correlation of .3 mean?
D. The correlation coefficient, denoted by r, is a measure of the strength of the straight-line or linear relationship between two variables. Values between 0 and 0.3 (0 and -0.3) indicate a weak positive (negative) linear relationship via a shaky linear rule.
Q. How do you interpret R squared value?
The most common interpretation of r-squared is how well the regression model fits the observed data. For example, an r-squared of 60% reveals that 60% of the data fit the regression model. Generally, a higher r-squared indicates a better fit for the model.
