stats you can do it

Question 1

What do measures of dispersion help researchers understand in a dataset?

Answer 1

How data points are distributed around a central value

Question 2

What does high variability in a dataset suggest about the reliability of the data?

Answer 2

lower reliability

Question 3

Which measure of dispersion focuses on the spread of the middle 50% of the data and excludes outliers?

Answer 3

i q r range

Question 4

Why is understanding variability critical when interpreting psychological research data?

Answer 4

It helps determine the representativeness of the sample and potential confounding factors

Question 5

What does a low standard deviation (SD) in a dataset indicate?

Answer 5

The data points are closely clustered around the mean

Question 6

Which of the following is an example of a correlation?

Answer 6

Income tends to increase with education level.

Question 7

What does a correlation coefficient tell you that a simple correlation does not?

Answer 7

The strength and direction of the relationship between two variables

Question 8

A correlation tells you how strong the relationship is between two variables.

Answer 8

true

Question 9

Coefficients are used in statistics to represent the relationship between variables. true false

Answer 9

true

Question 10

The correlation coefficient (Pearson’s r) measures the strength and direction of a linear relationship between two continuous variables.

Answer 10

true

Question 11

Higher values of Cronbach’s alpha indicate that the items measure the same underlying construct with good internal consistency.

Answer 11

TRUE

Question 12

When would you use a factorial ANOVA rather than a simple ANOVA to test the significance of the difference between the average of two or more groups?

Answer 12

Factorial ANOVA is used only when you have more than one factor or independent variable!

Question 13

If your tools aren’t reliable or valid, your results don’t really mean anything. You could end up drawing conclusions that are completely wrong, which is frustrating and a waste of time. That’s why reliability and validity matter so much—they’re the foundation of any good experiment. Without them, everything else falls apart.

Answer 13

what

Question 14

hen there’s validity, which is just as important. Validity means your tool is actually measuring what you’re trying to measure. Imagine trying to figure out how much you weigh but using a tape measure instead of a scale. Even if that tape measure works perfectly (super reliable), it’s useless for figuring out your weight because it’s not designed for that. Validity makes sure your results are meaningful and relevant to your hypothesis.

Answer 14

what

Question 15

We’d use a factorial ANOVA when we’re interested in looking at how two or more independent variables (factors) affect a single dependent variable—and whether those factors interact with each other.

For example, if you’re testing how teaching style (lecture vs. discussion) and study method (group study vs. solo study) impact test scores, a factorial ANOVA would help you see:

The individual effect of teaching style.
The individual effect of study method.
If there’s any interaction between teaching style and study method (like, maybe group study works better only with the discussion teaching style).

A simple ANOVA, on the other hand, just looks at one independent variable at a time. So if you’re juggling multiple factors, factorial ANOVA is the way to go!

Answer 15

what